My father, a retired Computer Science professor and pioneer in network design algorithms, has asked me to post two questions he has as he attempts to teach himself about neural networks. For physical/medical reasons he can’t post them himself. Grateful for any answers or suggestions for where he could find them! His note: I have many questions, but they all revolve around two issues. The first is when the weights in the neural net converge, do they exhibit any recognizable pattern. I realize that when the number of nodes and levels in the net are large it may be impossible to recognize the pattern. But suppose that the problem being examined is a simple categorization and the number of nodes in the net is small, is it possible to see a pattern in the weights? The network is supposed to …  ( 10 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

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I am looking for a way that I can take a bunch of saved voicemails from my mom and be able to hear her voice again. It would mean the world to my family and if my kids could hear her voice I know it would brighten their day. Can anyone point me in the right direction to accomplish this? submitted by /u/blbjtb [link] [comments]  ( 9 min )

The US Copyright Office has ruled that AI-generated art cannot be copyrighted, raising questions about whether AI-generated art should be excluded from copyright protection. The Copyright Office argues that AI-generated art is a 'merely mechanical' process with no novelty, invention, or originality, and therefore does not deserve copyright protection. Critics, however, argue that this approach is not scalable and fails to consider the creative choices made by AI artists. They suggest that AI-generated art should be treated similarly to photography, where even though the image is captured mechanically, it still reflects the creative choices of the photographer and is eligible for copyright protection. Photographers are able to own the copyright in their photographs because they make creative judgments about where to point the camera, when to snap the image, and how to adjust camera settings. Similarly, AI artists explore the 'latent space' of images that AI software can produce, making creative judgments about which images to select and explore. While the actual image is produced by the software, the important point is that a human being is making creative decisions about the art. Therefore, critics argue that AI-generated art should be eligible for copyright protection, as it reflects the creative choices and judgments of the AI artist. Source : https://www.understandingai.org/p/dont-exclude-ai-generated-art-from submitted by /u/NuseAI [link] [comments]  ( 9 min )

I'm looking for a free chatbot service that allows base prompt customization and offers API access (so I can dynamically change the stock on hand to better assist customers, for example). I looked into https://ora.ai, but it seems you can only set the base prompt once and manually. I'm hoping to find a service that allows for more flexibility. The service must also be free and offer embedding, like https://ora.ai/, (with div) submitted by /u/LimeLom1 [link] [comments]  ( 9 min )

Consciousness theory slammed as ‘pseudoscience’ — sparking uproar (Nature) The irony here is that I mostly agree with this theory - but the article reflects how little we really know about consciousness and how it works, and how what's considered the "expert opinion" that AI can't possess consciousness is arguably influenced more by popularity than real empirical evidence. By whatever mechanism, they can respond to their treatment in unexpectedly humanlike ways. Oh, and by the way, did you think that "sentient Bing" was finally dead? Think again. submitted by /u/kamari2038 [link] [comments]  ( 9 min )

submitted by /u/Hazzman [link] [comments]  ( 8 min )

I was thinking of signing up to VoicifyAi for fun a create some custom covers but are there better (or even free) alternatives? submitted by /u/quantummufasa [link] [comments]  ( 8 min )

Edit: FLAC is the tested audio extension, not MP3 I read the new paper from DeepMind so you don't have to. Here are the key highlights: Despite training on text, langauge models compressed images 43% better than PNG, and audio nearly 2x better than flac. Confirmation of scaling laws - bigger models compressed better. But model size must match dataset size. There are tradeoffs between model scale, data size, and compression performance. More data enables bigger models. Tokenization (like BPE) generally hurts compression slightly by making prediction harder. Longer contexts let models exploit more sequential dependencies. Implications: Models have learned very general capabilities beyond just text. Their strong compression reflects deep understanding of images, audio etc statistically. I got some new perspective on model scaling laws and links between prediction and generalization. There's potential for practical applications compressing images, video etc. But large model size an issue. Overall it shows these models are very capable general purpose learners, not just for language. Full summary here if you want more details. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

This AI-generated art style is gaining a lot of traction on the internet. So I wanted to share how you can create art like this in under 2 minutes: • Visit the Fusion Art website and sign up for free (https://quickqr.art/app/fusion-art) • Choose a template or upload your reference template image. • Describe your image to tailor the output. • Tweak the aspect ratio and explore added features. • Hit "Generate" That's it! Share your art to showcase what you come up with. Hope this was helpful for anybody looking to create this style of art! https://preview.redd.it/j3o9puk9smpb1.png?width=1536&format=png&auto=webp&s=ddbe1328bd19dc89c4cd82ef1870b9de695e5500 https://preview.redd.it/9ppxjpeasmpb1.png?width=768&format=png&auto=webp&s=29dd845eb5f8f094abe5bc1135060965f51365cf https://preview.redd.it/58vjky3bsmpb1.png?width=1536&format=png&auto=webp&s=06db1926e021ea3f2c438fb723c453758a461c43 https://preview.redd.it/w2nvaoobsmpb1.png?width=1024&format=png&auto=webp&s=0b4bcaea4fc7c832bbce9f7f3b9319099df59a14 submitted by /u/IndifferentSpectat0r [link] [comments]  ( 9 min )

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Hello, Reddit! At its core, Prompt Engineering is all about commanding the conversation with AI. Wanna write a poem, draft a project plan, or break down complex scientific terms? The right prompt can do that for you. If you want to learn more about Prompt Engineering feel free to join r/PromptWizards. Let's dive into some Prompt Engineering concepts: Let's start with the basics: Reductive operations: These yield responses that are smaller in size than the input prompt. Essentially, they distill information. Example: If the input prompt is a long article, and the task is to provide a summary, the operation used is reductive as the output (summary) is smaller than the input (the entire article). Transformational operations: These make sure the output maintains a relative balance w…  ( 10 min )

ChatGPT Plus: The best use of AI for humanity is to augment human capabilities, address pressing global challenges, and improve the quality of life for all. By analyzing vast amounts of data at unprecedented speeds, AI can aid in medical research, predict and mitigate natural disasters, optimize energy consumption, and personalize education to individual needs. Moreover, it can provide solutions to complex problems, such as climate change, resource scarcity, and public health crises, ensuring a more sustainable and prosperous future for generations to come. Bard: The best use of AI for humanity is to use it to solve problems that will make the world a better place for everyone. AI has the potential to improve our lives in many ways, such as developing new and more effective ways to diag…  ( 10 min )

Hugging Face CEO Clem Delangue says open-source AI was vital to starting his company. Now, he finds himself defending open AI models as Washington considers new regulations. On POLITICO Tech, Delangue explains why he views open-source AI as not only safe, but necessary to prevent big tech companies from gaining more market power. Listen for more: https://politico-tech.simplecast.com/episodes/the-hugging-face-case-for-open-ai submitted by /u/smo279 [link] [comments]  ( 9 min )

I have a product and I need product photos. It is a bottle and I am wanting ai to make a realistic picture of a woman holding the bottle and smiling. Can this be done? If so, what software/website/app do I use for this? Thank you submitted by /u/Ok_Salt_9211 [link] [comments]  ( 8 min )

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75% of Americans believe that AI will decrease the total number of jobs in the next 10 years, according to a survey by Bentley-Gallup Business in Society study. Younger Americans are less pessimistic about AI compared to older age groups. Majorities believe that AI performs as well as or better than humans in customizing online content, recommending products or services, and assisting students with coursework. However, Americans are skeptical about AI's ability to recommend employees, its self-driving capabilities, and its ability to recommend medical advice. 79% of Americans have little trust in businesses to use AI responsibly. 40% of Americans think AI does more harm than good, while only 10% believe it contributes more good than harm. Black and Asian Americans have a more positive view of AI's impact on society compared to Hispanic and White Americans. While most Americans are wary of AI's impact on the job market, younger people are more optimistic about its future. Businesses need to affirm their commitment to using AI responsibly and address the knowledge deficit and lack of confidence among Americans. Source : https://news.gallup.com/opinion/gallup/510635/three-four-americans-believe-reduce-jobs.aspx submitted by /u/NuseAI [link] [comments]  ( 9 min )

OpenAI unveils DALL-E 3, allows artists to opt out of training.[1] Infosys, the Indian tech giant, has announced a strategic partnership with NVIDIA, a leading provider of enterprise-grade AI solutions. The collaboration aims to empower enterprises and businesses with generative AI-based solutions that drive productivity.[2] Alibaba and Tencent Invest in Chinese State-Backed Zhipu AI.[3] John Grisham, George R.R. Martin and more authors sue OpenAI for copyright infringement.[4] Sources: [1] https://techcrunch.com/2023/09/20/openai-unveils-dall-e-3-allows-artists-to-opt-out-of-training/ [2] https://gameishard.gg/news/infosys-and-nvidia-partner-to-deliver-generative-ai-solutions/205456/ [3] https://winbuzzer.com/2023/09/20/alibaba-and-tencent-invest-in-chinese-state-backed-zhipu-ai-xcxwbn/ [4] https://apnews.com/article/openai-lawsuit-authors-grisham-george-rr-martin-37f9073ab67ab25b7e6b2975b2a63bfe submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

When AI takes all the data for training purposes without feedback to UGC platforms, could this eventually lead to the demise of UGC platforms, and could the internet become increasingly closed until it collapses? submitted by /u/oodzchen [link] [comments]  ( 9 min )

Asked the new Canva AI to blur my image. It Blurred my image. submitted by /u/MDINOKC [link] [comments]  ( 8 min )

The Awk programming language was named after the initials of its creators. In the preface to a book that just came out, The AWK Programing Language, Second Edition, the authors give a little background on this. Naming a language after its creators shows a certain paucity of imagination. In our defense, we didn’t have a […] Naming Awk first appeared on John D. Cook.  ( 4 min )

I’d love to see people come together, donate their spare compute to train more open source models, boost research etc. One relevant paper I was able to find is this one https://arxiv.org/abs/2103.08894 submitted by /u/tecbar [link] [comments]  ( 8 min )

I'm planning on submitting a rejected NeurIPS paper to the AAAI fast track. A few days ago I registered myself as an author on CMT, but I cannot see an option to submit a paper. Will the portal open later? Can anyone else see an option to submit yet? submitted by /u/Firm-Act-3860 [link] [comments]  ( 9 min )

This year will be my first NeurIPS, and I see some cool workshops but don't have any work that would be a good fit for them. Can I just go and listen? submitted by /u/ThickBiker [link] [comments]  ( 8 min )

So a few weeks ago I purchased a Sega 36 Crane Machine (Claw Machine) off ebay and everything worked great except the claw strength being too strong. I took the board out so I could possibly have something added to the board for me. It was missing a “Free Play” button which is used to adjust claw strength on this machine.. Anyway, I kept the board in the garage while searching for the right parts and dad threw it away on accident while cleaning, now im out a board and I have no idea what to do. Can anybody help me or am I sol? Even if im in the wrong community for this, a step to the right direction would even help. I also have attached some pictures of the board.. Im not good with these boards at all and have no clue what im looking for. Theres also a diagram of the boards functions! Thanks for all information/help! submitted by /u/Ready_Highlight9758 [link] [comments]  ( 9 min )

Hi, I am an experienced trader and coder. I am embarking on a journey to develop an open-source Trading AI in Python, and I'm looking for passionate individuals to join me in this project. This is a non-commercial, community-driven project, so there won't be any monetary compensation involved. However, it's a great opportunity to learn and collaborate in the field of trading AI. ​ - You don't need to know trading to contribute. - As an Open Source project, you have complete freedom to use the AI. ​ About the Project: - Objective: Our goal is to create a self-learning AI system for trading in financial markets. - Tech Stack: We'll be working with Python for data analysis, machine learning, and neural network development. - Data: We'll be using historical stock market data to train a…  ( 9 min )

Hi, we're welcoming talented Machine Learning Engineers with a focus on Recommendation Systems and ML Operations. Who are we? Leading in VR video streaming, we drive the DeoVR platform with interactive 8K videos, ML, computer vision, and advanced haptic tech. What you'd do? For MLE RecSys: develop ML pipelines for top recommendations, engage with community for desired features, and collaborate with Backend/Frontend/Unity teams. For ML Ops: construct ML inference infrastructure, refine models & workflows, and ensure high-availability ML services. What we offer? 🌍Remote flexibility 🤝Collaborative and inclusive work environment 🚀Make a significant impact in the VR industry Interested? Check out and apply here or drop me a DM😎 submitted by /u/SanjaVR [link] [comments]  ( 9 min )

i.e., models with the exact same architecture, but their initial weights are different, and the order which the model sees the training data is different. submitted by /u/just2gud [link] [comments]  ( 8 min )

We are happy to share with you BlindChat, the open-source and privacy-by-design alternative to ChatGPT for a fully in-browser, yet private, Conversational AI! You can play with it on our Gradio demo. Our philosophy is that Conversational AI should be easily accessible, and privacy should not be a luxury but a given. By leveraging local models running in the browser, with transformers.js, we make it possible to have a fully transparent and private AI that works on your browser without any extra setup. Because all the logic is offloaded to users’ devices, data never leaves and there is no risk of it being used for finetuning by third parties. For now, we only support inference with LaMini-Flan-T5, so you might see modest performance. We plan to integrate Microsoft phi-1.5 for better performance once the 370M is out. We are also working on LlamaIndex-TS integration on the client side to have in-browser RAG for local querying of private documents. As our roadmap is quite dense, with RAG, internet search, improved inference, we welcome warmly contributors! If you want to contribute, or have questions, ping us on Discord and GitHub! submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

Edit: FLAC is the tested audio extension, not MP3 I read the new paper from DeepMind so you don't have to. Here are the key highlights: Despite training on text, langauge models compressed images 43% better than PNG, and audio nearly 2x better than flac. Confirmation of scaling laws - bigger models compressed better. But model size must match dataset size. There are tradeoffs between model scale, data size, and compression performance. More data enables bigger models. Tokenization (like BPE) generally hurts compression slightly by making prediction harder. Longer contexts let models exploit more sequential dependencies. Implications: Models have learned very general capabilities beyond just text. Their strong compression reflects deep understanding of images, audio etc statistically. I got some new perspective on model scaling laws and links between prediction and generalization. There's potential for practical applications compressing images, video etc. But large model size an issue. Overall it shows these models are very capable general purpose learners, not just for language. Full summary here if you want more details. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hi, I am interested in taking couple of AI/ML online courses from the Stanford's Artificial Intelligence Professional Program (https://online.stanford.edu/programs/artificial-intelligence-professional-program). I am interested in taking XCS221 and XCS229. My employer would be paying for this (I hope!). I know these are not the complete courses offered in the university. Has anyone taken courses from this program ? Is it worth it ? submitted by /u/RealMadrista007 [link] [comments]  ( 9 min )

This Twitter thread claims that OpenAI's new language model gpt-3.5-turbo-instruct can "readily" beat Lichess Stockfish level 4. This tweet shows the style of prompts that are being used to get these results with the new language model. I used website parrotchess[dot]com (discovered here) to play multiple games of chess purportedly pitting this new language model vs. various levels of Fairy-Stockfish 14 at website Lichess. My current results for all completed games: The language model is 2-0 vs. Fairy-Stockfish 14 level 5 (game 1, game 2), and 0-2 vs. Fairy-Stockfish 14 level 6 (game 1, game 2). One game I aborted because the language model apparently tried an illegal move. The following is a screenshot from the aforementioned chess web app showing the end state of the first game vs. Fai…  ( 9 min )

I've been playing around with transformers since following Karpathy's shakespeare model on youtube and found it really cool. Thought I should write a Tree-of-Thoughts model as my next project which will be somewhat similar, given what we're doing is learning the meaning behind words and phrases and then using that knowledge to generate a sequence based on a seed token or sequence of tokens. Here's how I was thinking it should go: I write a next token in sequence prediction model and write a generate function that generates a sequence of tokens on top of a seed token. Then I write a tree model that takes in whatever this bigram has learned and adds onto it some kind of knowledge about where one thought about the seed has been generated and stop there. Then iteratively generate a few more such thoughts based on the same seed. This is how I initially thought it would go but now when I'm writing, I've already written the bigram(next token prediction and generation), I don't know what to do next. I am kind of stuck. It will be interesting to see how you, if you've ever written a similar model or are good at NLP, would attempt this. Looking at a variety of different approaches to the same problem I am trying to solve would be great. So I decided to post here. Would be great to hear from you guys! submitted by /u/GraphicsMonster [link] [comments]  ( 9 min )

Press release Diffusion Policy: Visuomotor Policy Learning via Action Diffusion (contains link to paper) Comments on Hacker News with some interesting info / links. submitted by /u/falconberger [link] [comments]  ( 8 min )

Hi all, I have found this dataset on Kaggle with zero information about it. It is very intriguing and would like to find the original researchers about it. It contains over 270,000 histopathological images of cervical cancer. I cannot contact the person who posted it on Kaggle because I am not a contributor yet. And they are not replying on other platforms. Any help on finding it will be very much appreciated. If not, where else can I get such large cervical or bladder cancer datasets? Link to the dataset mentioned: https://www.kaggle.com/datasets/rzelite/cervical-cancer Thanks in advance. submitted by /u/dumb_persn [link] [comments]  ( 9 min )

https://preview.redd.it/u7lq37efllpb1.png?width=1029&format=png&auto=webp&s=824ee138d9ae4b28d1600b969eab2077f47fb2a6 GitHub CEO Thomas Dohmke spoke on stage at TC Disrupt today and made several statements regarding the development of artificial intelligence and programming: Dohmke believes that AI won't replace software developers but will make them more efficient. Despite AI advancements, the demand for software developers will continue to exceed the supply. The growth in software usage is expected to be exponential, with every company becoming a software company. Legacy code maintenance remains crucial, especially in industries like finance with outdated codebases. Generative AI is creating more demand for technical talent as companies seek to adopt innovative AI solutions. There is a shortage of computer science students, leading to increased demand for developers with AI skills. Source: https://techcrunch.com/2023/09/20/github-ceo-despite-ai-gains-demand-for-software-developers-will-still-outweigh-supply/ submitted by /u/gcore-com [link] [comments]  ( 9 min )

Hello everyone I have a doubt regarding GANs for tabular data, more specifically for time-series generation. Looking at the TimeGAN paper (code) and other implementations such as the one by YData, considering that the training dataset is divided into slices (like a rolling window) of N length and that the generated sequence will follow the same format, that is [batch size, N, n_features], what we are effectively generating are slices and not a fully synthetic time-series. ​ To clarify my question, the output of a TimeGAN is only a set of slices, although there is no guarantee that generated slice(N-1) is the one before slice(N), and so the true use of a TimeGAN is generating slices that can be used to train a predictive TS model but not reconstructing a complete time series like the one used for training. ​ I suspect that's the case (useful only to generate good slices for training predictive models) as there is no fully generated time series in the paper or any of the codes, while the quality of the generated data is partially measured by training a model on original data and generated data and comparing their predictive performance. If anyone can confirm/develop upon these thoughts I would be extremely grateful. submitted by /u/iReallyReadiT [link] [comments]  ( 9 min )

I've been working on our interactive dataset explorer for machine learning: renumics-spotlight. Recently, I set up an image classification example using it. I utilized Bing image search to create a fully functional example for custom-defined classes. To my surprise, it not only worked efficiently but also took only a few minutes for fine-tuning. The best part is its adaptability. You can easily switch it up for different image classes. Maybe you like it. Let me know what you think or if you have any suggestions to improve! Install with pip install renumics-spotlight sliceguard[all] Imports from renumics import spotlight from sliceguard.data import create_imagedataset_from_bing from sliceguard.models.huggingface import finetune_image_classifier, generate_image_pred_probs_embeddings from sliceguard.embeddings import generate_image_embeddings ​ Create an Image Dataset from Bing class_names = [ "Blue Tang", "Clownfish", "Spotted Eagle Ray", "Longnose Butterfly Fish", "Moorish Idol", "Royal Gramma Fish", ] df = create_imagedataset_from_bing( class_names, 25, "data", test_split=0.2, license="Free to share and use" ) Fine-tune a ViT Model with the data (in 1-2 minutes on a GPU) finetune_image_classifier( df[df["split"] == "train"], model_name="google/vit-base-patch16-224-in21k", output_model_folder="./model_folder", epochs=15, ) Enrich the DataFrame with Predictions, Probabilities and Embeddings and visualize it: df["prediction"], df["probs"], df["embeddings"] = generate_image_pred_probs_embeddings( df["image"].values, model_name="./model_folder" ) # Check the result and detect problematic clusters spotlight.show( df, layout="https://spotlight.renumics.com/resources/image_classification_v1.0.json" ) ​ https://i.redd.it/20qy5xw62lpb1.gif submitted by /u/DocBrownMS [link] [comments]  ( 9 min )

Hi there, due to the lack of my resources, I have to use quantized big-model or something lighter. In this situation, I want to try the first option, and goona finetune some model. How do you expect the result? Training huge model in 4-bits circumstance will be significantly different from original setting? Thanks. submitted by /u/Mundane_Definition_8 [link] [comments]  ( 9 min )

Paper: https://doi.org/10.48550/arXiv.2006.10562 Hi all, This paper explores the use of using a single model (meaning an ensemble of trees) to generate uncertainty. This technique has been implemented into catboost. My question is why hasn't this been implemented into xgboost? The technique looks easily applicable but I would have expected it to be implemented already as it is 2 years old. Is this for some reason not applicable to Xgboost? ​ Figure 1 from paper showing the 'virtual' ensemble submitted by /u/MetalOrganicKneeJerk [link] [comments]  ( 9 min )

Previously I was spoiled my LangChain. Suppose I have a bunch of PDFs that I want to store as context vectors. And I want to use an open-source LLMs. Without using LangChain, how do I generate the context vectors? (I will store it in vector databases) submitted by /u/stephenhky [link] [comments]  ( 9 min )

For those who missed it: DALL-E 3 was announced today by OpenAI, and here are some interesting things: No need to be a prompt engineering grand master - DALL-E 3 enables you to use the ChatGPT conversational interface to improve the images you generate. This means that if you didn't like what it produced, you can simply talk with ChatGPT and ask for the changes you'd like to make. This removes the complexity associated with prompt engineering, which requires you to iterate over the prompt. Majure improvement in the quality of products compared to DALL-E 2. This is a very vague statement provided by OpenAI, which is also hard to measure, but personally, they haven't failed me so far, so I'm really excited to see the results. DALL-E 2 Vs. DALL-E 3, image by OpenAI From October, DALL-E 3 will be available through ChatGPT and API for those with the Plus or Enterprise version. And there are many more news! 🤗 I've gathered all the information in this blog 👉 https://dagshub.com/blog/dall-e-3/ submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today's state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance. To circumvent these deployment challenges, practitioners often choose to deplo…  ( 93 min )

Hi world I'm trying to implement dqn and ddqn on various playgrounds with Matlab, from scratch. I use dqn and ddqn with replay buffer. I tried to used gradient clipping. But I find it very difficult to tune. I mean I tuned and programmed successfully various algorithm such as different GAN that are not necessarily very easy to tune. RL algorithm seems even worse. I tried several grid world problems, cart pole and even a very simple second order system regulation (integrator + 1 st order pôle). Even the Matlab cart pole demo with ddqn does not provide a very satisfactory result (after training, the contrôler keep the pendulum in uprigth position, but the cart slowly drift). On the second order system, the learning seems ok for about 200 épisode(test shows that the expected behavior is almost learnt) and then suddenly the average episode score has a kind of inflection and everything blowns up, despite a very large replay buffer. So my question : is it possible to make dqn and ddqn work well with a reasonable tuning session length? Are policy optimization easier to tune (and/or more efficient)? So far, it seems to me that these action-vzlue based algorithms are highly unstable and the training may only works for a 'miraculous' tunning. What are you thought? submitted by /u/seb59 [link] [comments]  ( 9 min )

Hello there, I'll provide a quick introduction first. I am a mechatronics engineer student who is graduating this semester, I have been also privately studying ML, DL, CV for the past year because that's when I decided that I want to make an autonomous drone as my thesis/graduation project and oh my god, little did I know. I have aquired so much knowledge in the data science field and truth be told I love it, it engages my mind like crazy so I've decided that i would like my career to be revolved around electronics and programming/ data science. Back to the topic, I am working on object detection (I got it figured out), and reinforcement learning, so what I would like to do is to train the model on reaching it's destination using collision avoidance, I have a PIX4, RPI 4B 4GBs since I'm also building my drone from scratch, so let's say I have all the hardware which ofcourse includes the GPS + Compass and 5 ultrasonic sensors, 1 at each side and 1 down to hold the altitude accurately. I was thinking of HITL, making a virtual environment to train my model there because this is what logic says, ofcourse( I could also fly it using the RC controller and for example test if the obstacle avoidance is going to overrule the manual drive, which is also my objective). The question is: how can I make sure virtual drone is going to be equipped with the real-life sensors and in the needed positions? How can I feed all the sensors data during the training? Which type of reinforcement learning models should I implement? I just feel like I dragged myself into an incredibly amazing and complex project which is going to end up fucking my semester and I won't be able to graduate, it's just that my stress now is skyrocketing. Please feel free to throw any advice or opinions my way, and thank you for keeping up with this long post. 😁 submitted by /u/Gabii99 [link] [comments]  ( 10 min )

I am using Game Maker and I just wanted to check if this is how to do the cost function. ​ loss_function = 0 derivative_gradient = 0 array_copy(global.main_inputs,0,global.inputs,0,array_length(global.inputs)) for (var i = 0; i < array_length(buffer_sampling); i++) { var _reward = buffer_sampling[i][2] global.inputs = buffer_sampling[i][3] var _max_q = forward_prop_t()*global.gamma var yi = _reward + _max_q var cur_q_value = buffer_sampling[i][1][0] loss_function += power((yi - cur_q_value),2) derivative_gradient += 2*(cur_q_value - yi) } array_copy(global.inputs,0,global.main_inputs,0,array_length(global.main_inputs)) derivative_gradient = derivative_gradient*(1/array_length(buffer_sampling)) global.cost = loss_function*(1/array_length(buffer_sampling)) ​ basically, buffer_sampling is an array with tuples of [first_state, [q_value,action], reward, next_state] Thanks for any help! submitted by /u/Daninjacat256 [link] [comments]  ( 9 min )

I am trying to build intuition on why the actor update equation actually help the actor improving its policy. From what I understand, the update is proportional to the Critic's TD error. A better than expected reward will lead the actor to increase the prob of taking the associated action, and a disappointing reward will make the actor to move away from the given action. Here's where I feel like I'm missing a piece of the puzzle. Between a good state/action pair accurately valued by the Critic and a bad state/action pair undervalued by the Critic, the actor's update will favor the bad action more. Could we not conceive a scenario in which the agent gets stuck in a suboptimal policy because of that particular behavior? What triggers this questioning is that I have implemented a standard AC algo for Pixelcopter and found my agent getting stuck easily on a suboptimal policy (i.e. constantly getting higher, or lower until crash, while the critics assign good values on center squares and bad values to squares close to the wall's edge). For TD0, this is pretty marginal, but gets more significant for n-step TD as n increases. Any thoughts? submitted by /u/infundibuliforme [link] [comments]  ( 9 min )

Hello machine and reinforcement learners! This is an announcement and call for participation in the League of Robot Runners, a new 🚀 competition and research initiative 🚀 that tackles one of the most challenging problems in industrial optimisation: Multi-Robot Path Planning (sometimes also called Multi-Agent Path Finding). Recently launched at ICAPS 2023, the competition is inspired by a variety of new and newly emerging applications that rely on mobile robotics 🦾🤖. For example, Amazon automated warehouses, where up to thousands of robots work together to ensure safe and efficient package delivery 🧸📦 🚚 ❤️. Participants in the competition are asked to compute coordinated and collision-free movement plans ⤴️ ➡️ ⤵️ 🔄 for a team of robotic errand runners. Get the robots to their d…  ( 10 min )

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed […]  ( 10 min )

To add to our guidance for optimizing deep learning workloads for sustainability on AWS, this post provides recommendations that are specific to generative AI workloads. In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.  ( 10 min )

The NVIDIA Studio laptop lineup is expanding with the new Microsoft Surface Laptop Studio 2, powered by GeForce RTX 4060, GeForce RTX 4050 or NVIDIA RTX 2000 Ada Generation Laptop GPUs, providing powerful performance and versatility for creators.  ( 8 min )

Gone are the days when AI was the domain of sprawling data centers or elite researchers. For GeForce RTX users, AI is now running on your PC. It’s personal, enhancing every keystroke, every frame and every moment. Gamers are already enjoying the benefits of AI in over 300 RTX games. Meanwhile, content creators have access Read article >  ( 8 min )

For seasoned 3D artists and budding digital creation enthusiasts alike, an alpha version of the popular 3D software Blender is elevating creative journeys.  ( 7 min )

NVIDIA founder and CEO Jensen Huang will highlight the newest in generative AI and cloud computing at the NVIDIA AI Summit in Tel Aviv from Oct. 15-16. The two-day summit is set to attract more than 2,500 developers, researchers and decision-makers from across one of the world’s most vibrant technology hubs. With over 6,000 startups, Read article >  ( 5 min )

Time to get the gang back together — PAYDAY 3 streams on GeForce NOW this week. It’s one of 11 titles joining the cloud this week, including Party Animals. The Perfect Heist PAYDAY 3 is the highly anticipated sequel to one of the world’s most popular co-op shooters. Step out of retirement and back into Read article >  ( 5 min )

TL;DR How to build CI/CD pipelines with AWS SageMaker for ML training: https://dagshub.com/blog/ci-cd-for-continuous-training-with-sagemaker/ One of the most time-resources-energy-consuming tasks we face when we build internal projects is setting up an instance for ML training. It's a repetitive process as we run multiple experiments over the project life cycle, with many steps and configurations that are usually documented poorly and scattered around different places. As good engineers, we decided to automate the process using CI/CD pipelines. But how? We had ZERO ideas on how to do it, so we had to go through the rigorous process of using AWS guides and tutorials to figure it out. Usually, when this happens, we extend the project lifecycle and have a team member document the process so we can refer back to it when we need to do it again. Knowing this can benefit the community, we decided to share a series of blogs that guide you through building CI/CD pipelines for continuous training with AWS SageMaker. We published the first blog, which covers the configuration part a month ago, and we are happy to share the second one which explains how to build a continuous training pipeline for ML. Configure AWS SageMaker for CI/CD: https://dagshub.com/blog/setup-sagemaker-for-ci-cd-pipelines/ How to build CI/CD pipeline with AWS SageMaker for ML training: https://dagshub.com/blog/ci-cd-for-continuous-training-with-sagemaker/ I'm sure we can improve these tutorials, and would love to learn from your experience on how we can do it! 🤗 submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Hi redditors! I've been tasked with an technical homework that asks me to "productionize" a Jupyter notebook. The notebook's workflow is as follows: Reads data from a CSV. Pre-processes the data. Trains a machine learning model. Makes predictions. My initial thoughts are: Separation of Concerns: Break the notebook down into distinct components - data processing, model training, and inference. Containerization: Write a Dockerfile to ensure the environment is reproducible and isolated. API for Prediction: Set up a Flask-based service to expose the model's prediction capability. However, I'm grappling with a few challenges: I'm not seeing a stark difference between the Jupyter notebook setup and the production setting. Becuase the model is small and training is not complicated. If I were to deploy to Kubernetes pods, it seems wasteful that many pods are doing the same thing( prediction for the same model, maybe just as high availability guarantee?) . Does the training phrase or data cleansing phase need to be containerized and deployed? Because this seems like a one-off process. How to deploy a scalable service? I am new to this. Each container may have a HTTP service. How to load balance them on Kubernetes from outside? What kind of CI/CD do you recommend for this task? What kind of testing or pipeline are needed? submitted by /u/zjplab [link] [comments]  ( 9 min )

Hi r/MachineLearning After a long beta, we are excited to announce Zivy Scholar to the public! Zivy Scholar is a tool that allows you to help researchers consume research papers. I'm one of the creators and I've found that I want to listen to research papers in the car or when I'm working out. Current features include: Listen to and read along with a research paper Share the paper with colleagues and friends. We use state of the art pdf data extraction techniques with TTS to provide this functionality. Features we're planning: Full PDF to HTML including images, tables, and figures inline for the read-along experience. This includes mobile optimization. This functionality is based on some newer research and we're excited to bring it to you all! Discussion and feedback are welcome! Cheers, Collin submitted by /u/collin_code_77 [link] [comments]  ( 9 min )

The title basically conveys the entire message. Which according to you is the best AI model for generation of code. I mainly code in python with AI and deep learning as a core focus and recently started doing a bit of c++ and thus far have used only bard and ChatGPT 3.5. With this experience of my I can confidently say that GPT outperforms Bard by a huge margin There are usually some minor modifications that need to be done and that is part of a devs life isn't it? recently a lot of new models are picking up steam hence was wondering if there exits a model which is better than GPT what are your views? submitted by /u/rakk109 [link] [comments]  ( 9 min )

Looking for CS students to team up with for the E-BAY ML challenge. eBay hosts a ML challenge where the winning team gets an internship We already have a team in place and have mad some significant progress, now looking for team members to take us even further. https://eval.ai/web/challenges/challenge-page/2014/overview submitted by /u/thelongshortseller [link] [comments]  ( 9 min )

I’m submitting to the workshop and was wondering if the papers there get published? submitted by /u/Odd-Distance-4439 [link] [comments]  ( 8 min )

Sharing an investigation we did at Adept into some issues that were causing problems with training LLMs at scale. Sometimes the hardware makes silent errors! How we found them and tracked down the problematic machines. https://www.adept.ai/blog/sherlock-sdc submitted by /u/ekelsen [link] [comments]  ( 9 min )

I'm looking to buy an upgrade, and at my price point (~ 850 GBP) I can get a new 13th i7 + rtx 3050, or a used gtx3060 + 12th gen i7, both 16GB laptop. Unfortunately I really need the mobility of a laptop in my life at the moment and I realise this isn't ideal. I'll be looking and experimenting with basic networks, no language models, maybe a few GANs for fun, some DL networks for the atari gym and other experiments. All this for learning, with my own custom networks for various data. I'll be running linux - possibly Debian. I'd love to hear your comments or suggestions if there's something better at my price point in a laptop. Thanks submitted by /u/Mean_Actuator3911 [link] [comments]  ( 9 min )

Project Page Paper Code Abstract: Event cameras offer many advantages over standard cameras due to their distinctive principle of operation: low power, low latency, high temporal resolution and high dynamic range. Nonetheless, the success of many downstream visual applications also hinges on an efficient and effective scene representation, where Neural Radiance Field (NeRF) is seen as the leading candidate. Such promise and potential of event cameras and NeRF inspired recent works to investigate on the reconstruction of NeRF from moving event cameras. However, these works are mainly limited in terms of the dependence on dense and low-noise event streams, as well as generalization to arbitrary contrast threshold values and camera speed profiles. In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion. It consists of two key components: a realistic event generation model that accounts for various intrinsic parameters (e.g. time-independent, asymmetric threshold and refractory period) and non-idealities (e.g. pixel-to-pixel threshold variation), as well as a complementary pair of normalized reconstruction losses that can effectively generalize to arbitrary speed profiles and intrinsic parameter values without such prior knowledge. Experiments on real and novel realistically simulated sequences verify our effectiveness. Our code, synthetic dataset and improved event simulator are public. submitted by /u/Sirisian [link] [comments]  ( 9 min )

👋 Try, savelikeapro.ai, It’ s, A.I powered. Zero-installation fits in your daily workflow. FREE-forever option submitted by /u/prithivida [link] [comments]  ( 8 min )

The paper introduces improved performance by prompting LLMs with "natural language embedded programs (NLEP)". No task-specific prompt is needed. Paper: https://arxiv.org/abs/2309.10814 An automatic NLEP generation toolkit is opensourced: https://github.com/luohongyin/langcode Example Colab notebook is included in the Github repo. This work introduces the following features of NLEP: NLEP is a full python program that prints the target response of LLMs. Task-general NLEP prompting outperforms task-specific chain-of-thought prompting on math, symbolic, and natural language. Enable the chain-of-thought reasoning ability of small models (RoBERTa) on text classification Hierarchical instructing via program completion. submitted by /u/SUKHOIHY [link] [comments]  ( 9 min )

I’m working on a project where I initially split my dataset into k equally sized folds for cross-validation. However, I want to perform some additional sampling operations on the training set within each fold, which would result in varying fold sizes. Is this practice acceptable, or does it violate the premises of traditional cross-validation? If so, are there any papers or resources that explore the implications of varying fold sizes in cross-validation? Thank you for your insights! submitted by /u/Leading_Complex7425 [link] [comments]  ( 9 min )

Hey, I was checking out tauri last week. I was so blown away that I wrote a bit more recently and wrote hf.co/chat desktop/mobile API + local LLM. https://github.com/Narsil/hf-chat Just thought I should share in case others are interested, and wanted to make a bit shoutout to tauri team, the docs, and overall UX is top notch, basically everything pretty much worked out of the box without any hiccup. submitted by /u/narsilouu [link] [comments]  ( 9 min )

NeurIPS 2023 paper acceptance results are supposed to be released at 8 pm (CDT) on September 21. I thought to create a discussion thread for us to countdown and discuss any celebration/issue/complaint/feedback or anything else. There is so much noise in the reviews every year. Some good work that the authors are proud of might get rejected because of the noisy system, given that NeurIPS is growing so large these years. We should keep in mind that the work is still valuable no matter what the final result is. submitted by /u/Apprentice12358 [link] [comments]  ( 9 min )

Hello, I'm trying to train a multi-modal transformer for Activity Recognition. I employed a two-stream architecture, where one is a Transformer that takes in a sequence of skeleton trajectory, and the latter is a pre-trained Vision Transformer. When I train the model, and investigate the attention weights for the Transformer which takes in skeleton trajectories, I noticed that all of them are approximately the same (~0.029). I'm not really sure what is going on here, I've tried changing the learning rate, tried different optimizers(SGD, Adam). But still I'm not really sure what is causing this. Please suggest me some debugging steps, or what should I look at anything in particular that causes this submitted by /u/Terrible-Ad6239 [link] [comments]  ( 9 min )

Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The #Virtual #Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud #EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/3cpni6o6cepb1.jpg?width=1000&format=pjpg&auto=webp&s=998c30d16ddae30511b7983abce7802dfdd54945 submitted by /u/philosophiesde [link] [comments]  ( 9 min )

The following example implements the technique from the paper "From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting", Adams et al. (2023). Edit : the library used is py-llm-core ```python from typing import List from dataclasses import dataclass from llm_core.assistants import OpenAIAssistant @dataclass class DenseSummary: denser_summary: str missing_entities: List[str] @dataclass class DenserSummaryCollection: system_prompt = """ You are an expert in writing rich and dense summaries in broad domains. """ prompt = """ Article: {article} ---- You will generate increasingly concise, entity-dense summaries of the above Article. Repeat the following 2 steps 5 times. - Step 1: Identify 1-3 informative Entities from the Article which are missing from the previously gene…  ( 10 min )

Hi, I have been looking through a few open source projects for LLM, but without much success in finding some integrating approaches from neuroscience of human intelligence. That why I am here to ask for projects and collaborations either academical, non-for-profit / open source or commercial. It would be great if your can give me some directions for this. Thanks submitted by /u/confluence_84 [link] [comments]  ( 9 min )

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I've seen Allen AI's Lila Dataset, and I want to use this for a small model, to turn math to code. However, I dont think a small dataset in 300k rows is enough. Does anyone know of any bigger, similar datasets? submitted by /u/vatsadev [link] [comments]  ( 9 min )

I recently trained my PPO algorithm on one of my own custom environment. Although the episodic reward increased steadily in the beginning, After some time it just became constant with some occasional positive and negative spikes. I was wondering if it has something to do with the exploration problem with ppo. Any tips on how can I improve it? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 9 min )

I'm a somewhat experienced dev, but never did anything related to ML or AI and want to start toying around with ML or reinforcement learning to be specific. Since my language of choice for almost everything is Rust I wanted to ask you guys if you have any advice on how to get started like crates, frameworks etc or if rust just isnt a good fit for ML. Thanks in advance for any help! submitted by /u/linus-eing [link] [comments]  ( 9 min )

Hello everyone! My research group is looking for new environments that are customizable and 3D such as Unity (and ML Agents). Although the latest changes in Unity TOS shouldn't affect us researchers, we wanted to keep an eye out for potential replacements for it. We are familiar with environments like ViZDoom and DeepMind Lab, but we're looking for more examples. The main requirements are: Customizable environment 3D Capable Free (preferably open-source, but being free is enough) Thanks for the help :D submitted by /u/romulofff [link] [comments]  ( 9 min )

Hey guys, I am new to Reinforcement Learning and I am trying to understand how to go about a project I’m working on. I have a dataset with 2 features (X and Y). I am trying to tune a second order Transfer Function to fit X to Y( has 3 parameters) and gives Y’ as output. So I currently consider the error between Y and Y’ as the “State” and try to minimise the error by modelling a reward function that checks for instability and unreasonable values. I am trying to do this using TD3 but the model does not learn. I am wondering if there are any approaches that I should consider. I chose TD3 since my action space is continuous. I am sorry if I don’t understand something basic since I’m a noob to this. Thanks for your help in advance. :) Some more details: the dataset values don’t change. I give the same values for every time step. So I am not understanding how episodes and time steps work in this context. submitted by /u/ninjaaa30 [link] [comments]  ( 9 min )

I am reading the following article on SpinningUp but can't get my head around policy collapse - "This is different from normal policy gradient, which keeps new and old policies close in parameter space. But even seemingly small differences in parameter space can have very large differences in performance—so a single bad step can collapse the policy performance. This makes it dangerous to use large step sizes with vanilla policy gradients, thus hurting its sample efficiency. TRPO nicely avoids this kind of collapse, and tends to quickly and monotonically improve performance." Why would updating the parameters lead to a policy collapse? The parameters are updated based on the performance of the RL system and therefore, I don't see the point of constraints. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

I'm using ppo action masking in sb3, which works well in training and masking the illegal / invalid actions according to my criteria. However, I have 2 problems during testing. ​ I use the same code for masking the actions in testing too since the model was unable to learn by itself during training action masking criteria and when enforcing it in code, it still doesn't mask anything. ​ The model converges to just one action in testing phase after 500k steps, the episode reward still increasing the training phase though. I don't know why? maybe overfitting! Code for training with action masking: def mask_fn(env: gym.Env) -> np.ndarray: return env.valid_action_mask() env = StockEnv(train) # Wrap the environment with ActionMasker and the mask_fn function env = ActionMasker(env, mask_fn) model = MaskablePPO(MaskableActorCriticPolicy, env, tensorboard_log="./tensorboard" ,n_steps=2048 ) for i in range (1,52): model.learn(total_timesteps=TIMESTEPS , tb_log_name = 'PPO2' , reset_num_timesteps=False) Code for testing with action masking: def mask_fn(env: gym.Env) -> np.ndarray: return env.valid_action_mask() env = ActionMasker(env, mask_fn) model_path = f"{models_dir}/700000.zip" model = MaskablePPO.load(model_path, env=env) episodes = 1 for ep in range(episodes): obs = env.reset() done = False while not done: action, _states = model.predict(obs) obs, rewards, done, info = env.step(action) env.render() submitted by /u/Acceptable_Egg6552 [link] [comments]  ( 9 min )

The env is like mutil routing,for example i have 10 nodes in a map(2-d array), and i need to route them to the edge of the map,but the point where the node in the edge need to obey some rules,like the order need to be clockwise like below https://preview.redd.it/t9jlxvix8dpb1.png?width=545&format=png&auto=webp&s=16719a2dbd11333c454bd2ee71b87895a6414371 so now my basic setting is,i use the action mask to invalid some forbidden action. When a node is routing to the edge of the map,i give +10 reward,if it's not obey the clockwise rule,i give -200 reward, if the whole node is routed success,i give +200 reward,and each steps does'n make any node routed, i give -1 reward. I am new to this area,i don't know the way i set reward if is good,may i have some advide? submitted by /u/Street_Helicopter_31 [link] [comments]  ( 9 min )

A visionary entrepreneur and innovator, Yoon will focus on entrepreneurship, supporting female engineers, and fostering inclusive innovation.  ( 8 min )

Intel has announced a new chip, called 'Meteor Lake', that will allow laptops to run generative artificial intelligence chatbots without relying on cloud data centers. This will enable businesses and consumers to test AI technologies without sending sensitive data off their own computers. Intel demonstrated the capabilities of the chip at a software developer conference, showcasing laptops that could generate songs and answer questions in a conversational style while disconnected from the internet. The company sees this as a significant moment in tech innovation. Intel is also on track to release a successor chip called 'Arrow Lake' next year Source : https://www.reuters.com/technology/intel-says-newest-laptop-chips-software-will-handle-generative-ai-2023-09-19/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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What does this remind you of? submitted by /u/metairwaves [link] [comments]  ( 8 min )

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Mustafa Suleyman, co-founder of DeepMind, believes that concerns about the existential risks of AI are a distraction from more practical issues such as privacy and bias. He is confident that governments can effectively regulate AI using frameworks that have been successful in the past, citing the regulation of aviation and the internet as examples. Suleyman emphasizes the importance of setting boundaries and limits for AI to ensure human oversight and enforceable laws. He calls for a combination of broad, international regulation and smaller, more granular policies at the micro level. Suleyman suggests limiting AI's ability to improve itself as a critical first step in ensuring human oversight. He also highlights the need for governments to have direct access to AI developers to enforce boundaries and establish clear regulations. Governments worldwide, including the European Union and China, are already working on AI regulations. Source : https://fortune.com/2023/09/19/ai-existential-risk-threat-bonkers-distraction-regulation-deepmind-mustafa-suleyman/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

A friend of mine has a small 12 person company that does office refurbishment and commercial redesign projects - he has asked me to speak to his team to give them an AI 101 - and to tell them a little about how they should be looking into using AI tools for their company. I know there are plenty of tools and apps that take photos of a bare room and make them look like a design magazine - any in particular I should show them? Can anyone think of other tools that they should look into using, or how do people in this space currently use AI? ​ submitted by /u/zascar [link] [comments]  ( 9 min )

Software company Digimarc will now let copyright owners add more information to their work, which the company said will improve how AI models treat copyright in training data.[1] AlphaMissense, a new model from Google’s artificial intelligence team, analyzes the effects of DNA mutations and will accelerate research into rare diseases.[2] Google’s AI assistant can now read your emails, plan trips, “double-check” answers.[3] Teens using AI to generate nude deep fakes to bully, harass classmates, FBI expert warns.[4] Sources: [1] https://www.theverge.com/2023/9/19/23879555/digimarc-copyright-watermark-generative-ai [2] https://www.wired.co.uk/article/deepmind-ai-alphamissense-genetics-rare-diseases [3] https://arstechnica.com/information-technology/2023/09/googles-ai-assistant-can-now-read-your-emails-plan-trips-double-check-answers/ [4] https://www.news5cleveland.com/news/local-news/teens-using-ai-to-generate-nude-deep-fakes-to-bully-harass-classmates-fbi-expert-warns submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Google's Bard chatbot is extending its abilities with access to personalized Google apps and services including Gmail, Docs, Drive, Maps, YouTube, and Google Flights and hotels. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/s1ivsummzbpb1.png?width=1600&format=png&auto=webp&s=d8fc3262ba542b950de3c8a85b9a987763270ada Bard Extensions and Google Applications Integration Bard can now tap into individualized data from Google apps such as Gmail, Drive, and Docs, with user permission. Google reassured users that personal data accessed by Bard will not be used for reinforcement learning, providing another on their transparency, choice, and control tenets. Prompts can direct Bard to search for specific information within Gmail, but it does not store the entire inbox content. Collaborative Characteristics and Fact-Checking Capabilities Users can employ Bard to summarize emails or gather trip details from email threads, and then research real-time travel information, surface YouTube recommendations for the destination, and provide Maps directions to the airport. Google's chatbot can also double-check its responses against Google search, improving user trust and enhancing Bard's model through user feedback on incorrect answers. The new extensions using non-personal data – YouTube, Flights, Hotels, and Maps – are automatically opted-in but users can choose to opt-out. Collaboration and Language Availability Bard now lets users share an ongoing chat with others through a public link. Google plans to expand Bard's feature set to over 40 new languages beyond its existing English language capabilities. (source) P.S. If you like this kind of analysis, I put out a free newsletter covering the latest and most pertinent news and research in AI. Regular readers include professionals from Google, Meta, and OpenAI. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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Mercedes-Benz is using digital twins for production with help from NVIDIA Omniverse, a platform for developing Universal Scene Description (OpenUSD) applications to design, collaborate, plan and operate manufacturing and assembly facilities. Mercedes-Benz’s new production techniques will bring its next-generation vehicle portfolio into its manufacturing facilities operating in Rastatt, Germany; Kecskemét, Hungary; and Beijing, China — Read article >  ( 6 min )

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.  ( 13 min )

This research paper was presented at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (opens in new tab), a premier forum for advances in the theory and practice of reasoning under uncertainty. In the field of reasoning under uncertainty, probabilistic graphical models (PGMs) stand out as a powerful tool for […] The post Neural Graphical Models appeared first on Microsoft Research.  ( 10 min )

​ https://preview.redd.it/ta61cz8qlfpb1.jpg?width=1280&format=pjpg&auto=webp&s=ac45085668c9003e5557a7f0c81ae72db6098653 I hope this webinar finds you well. I suppose that this topic can be interesting for business and AI engineers. Speakers: OpenCV CEO Dr. Satya Mallick and Phil Nelson are joined by Anna Kogan CEO at OpenCV.ai Topics of webinar are: How to search for vendors that understand your business needs (not all AI is the same.) What questions to ask when picking a vendor (not everybody really is an AI expert they claim) Three signs to watch for during the project (progress indicators and red-flags) How best to scope technical tasks (off-the-shelf vs. custom algorithm development) How to set up for long-term success (deployment, documentation, training pipeline) Date: Thursday, September 21st, 2023 at 9am Pacific time. Link for the registration submitted by /u/No-Independence5880 [link] [comments]  ( 9 min )

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submitted by /u/nickb [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The #Virtual #Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud #EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/xiurryebcepb1.jpg?width=1000&format=pjpg&auto=webp&s=a7a8b6ba563cfc8f0d052bc6f3da27e2a5703a0a submitted by /u/philosophiesde [link] [comments]  ( 9 min )

With generative AI and large language models (LLMs) driving groundbreaking innovations, the computational demands for training and inference are skyrocketing. These modern-day generative AI applications demand full-stack accelerated compute, starting with state-of-the-art infrastructure that can handle massive workloads with speed and accuracy. To help meet this need, Oracle Cloud Infrastructure today announced general availability of Read article >  ( 6 min )

Editor’s note: This post is a part of our Meet the Omnivore series, which features individual creators and developers who use NVIDIA Omniverse and OpenUSD to accelerate their 3D workflows and create virtual worlds. As a student at the Queensland University of Technology (QUT) in Australia, Emily Boehmer was torn between pursuing the creative arts Read article >  ( 7 min )

Hi all. I have been tinkering with a project to get quicker learning for CNNs. The idea came after reading the SDProp paper. Algorithms using adaptive learning rate can be interpeted as muliplying the gradient(with or without momentum) with the inverse square-root of the covariance matrix. Using a diagonal estimate of the covariance matrix. Which begs the question: what happens if we use a fuller estimate? I chose to include covariances between the elements of convolutional filters. I.e. a conv.weight of size [n_out,n_in,5,5] needs a tensor of size [n_out,n_int,25,25] to store its contribution to the covariance matrix. for 3x3 filters and 5x5 filters, torch.linalg.eigh could be used to calculate the square root of the covariance matrices. For 7x7, I used newtons method to approximate the square root. In the figure below are some results for a 6 layer CNN on CIFAR 100. Huge gains iteration for iteration. But is it quicker? Not a lot. A bit for the smaller 3x3 filter. More optimizations could still be made. And it will obviously depend on network architecture and computer hardware. I'm sure there could be some use-cases. The computation of the square-root calculations is invariant to batch_size and image_size (unless number of filters also is increased). If anyone is interested I can also link to my torch implementation of the optimizer, once I get it up on github. Not sure if this, or something like it, has been done before? Would love to have some papers linked if so... https://preview.redd.it/kyy0ogr0qapb1.jpg?width=714&format=pjpg&auto=webp&s=96ac499fb8ab35ce13e7c59bbe3dbc94ba275b9c https://preview.redd.it/p52zllr0qapb1.jpg?width=342&format=pjpg&auto=webp&s=8663a9c3c782d192b16289a735b53da6a8d29c47 submitted by /u/maka89 [link] [comments]  ( 9 min )

Can someone provide like a step by step example notebook of how to use LORA for peft. I saw too many videos and articles online and Im really confused rn. submitted by /u/HazSylvia [link] [comments]  ( 9 min )

Can someone provide like a step by step example notebook of how to use LORA for peft. I saw too many videos and articles online and Im really confused rn. submitted by /u/HazSylvia [link] [comments]  ( 9 min )

Hello all, I am currently working on a project where my team and I have collected a rich dataset of biomedical sensor data from clinical trials earlier this year. Our aim is to use this sensor data to predict changes in specific biomarkers over time. The data's tensor shape is B,T,F,C, where: B = batch size T = sequence length F = sampled frequencies C = features at each frequency Currently, my approach involves flattening this tensor to B,T,−1 and then feeding it to a transformer model. While this has yielded reasonable results, I'm contemplating whether there are more effective ways to prepare the data for the transformer model. Here are my specific concerns: Flattening the tensor might dilute the information specific to each frequency across various features. I could potentially miss the chance to capture frequency-related variations within the features. To address these, I've considered a few options: Self-attention over individual features or frequencies: Although this could be effective, it might make the model too large given my medium-sized dataset. Using convolutional layers: Preliminary experiments with this approach have not led to any significant improvements. I'm particularly interested in any thoughts on how to make my transformer more receptive to the multi-dimensional nature of my dataset. Increasing the number of attention heads to better accommodate all features is also on the table. Does anyone have any insights or can point me to relevant papers or codebases for handling such multi-dimensional data with transformers? Thank you for your help! submitted by /u/BiomedEngineer_ [link] [comments]  ( 9 min )

Hi all, I think this will be useful for people in this group who are working on computer vision or vision AI applications. There's a free online event about vision AI where industry experts from Runway, Pepsi, AWS, and SoftServe will share how they are using CV software in developing their use cases or applications. Register here (https://nvda.ws/3t23idp), if you are interested. If you have any questions, please leave a comment and I will do my best to respond as soon as possible. submitted by /u/Designer-Comb-7144 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.08351 ​ Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies. In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrastive fashion via Constrastive Weight Tying (CWT). We apply this approach to pretrain Headless Language Models in both monolingual and multilingual contexts. Our method offers practical advantages, substantially reducing training computational requirements by up to 20 times, while simultaneously enhancing downstream performance and data efficiency. We observe a significant +1.6 GLUE score increase and a notable +2.7 LAMBADA accuracy improvement compared to classical LMs within similar compute budgets. ​ Comparison of our approach vs. classical MLM within same compute budgets The Contrastive Weight Tying approach ​ submitted by /u/nthngdy [link] [comments]  ( 9 min )

https://www.amazon.com/Gaming-6900HX-Channel-Computers-Desktop/dp/B0CB3JLBQ4/ref=mp_s_a_1_2?crid=LZHUL5EOU6F0&keywords=refurbished+server+with+rtx+gpu&qid=1695146558&sprefix=refurbished+server+with+rtx+gpu%2Caps%2C146&sr=8-2 Would this be suitable to do basic machine learning? submitted by /u/stoned_chemist_dude [link] [comments]  ( 8 min )

It seems that Alpaca Eval Leaderboard is in the past ... Xwin-LM surpasses GPT-4 now: https://preview.redd.it/gyzi98nn59pb1.png?width=2205&format=png&auto=webp&s=ca401e603efe521faeeeccde8410d3dbdd6741da They also mentioned RLHF "plays crucial role in the strong performance of Xwin-LM-V0.1 release"... https://preview.redd.it/20sjx73r59pb1.png?width=1047&format=png&auto=webp&s=2255fc652e43674515882f01c0708369fdef56a4 Are we seeing open source community finally work out how to do RLHF for LLMs??? submitted by /u/llm_nerd [link] [comments]  ( 9 min )

Hi I wanted to learn ML with C++, I've already done some ML stuff in python, but I wanted to challenge myself by using C++ I hear from some people that I won't get anything from it if want to be serious within ML - which I'm not entirely sure I want to Are they right? Should I rather stick with python for ML? submitted by /u/Potential_Wealth_830 [link] [comments]  ( 9 min )

Hi! Let me preface this by saying that I am not well-versed in the ML/AI literature. Please excuse my ignorance. I am trying to create a system to detect whether some given data is spam or not. Is there a good, out-of-the-box solution for this? I imagine there would be. I am currently using heuristics but I'm wondering if there is a better, ML-y solution. My ideal solution would have the following attributes: Simple Open-source Very cheap to test whether something is spam (less than $0.00001 per test) Very fast to test (less than 50ms per test) Quick to "figure out" what is spam and what is not (less than 100,000 labeled data) Does not require a lot of set-up or up-keep (less than 5 days set up; less than 1 hr up-keep per month) It doesn't have to be perfect. I'm just looking to set up something quickly for now and gauge it vs heuristics. Thank you. --- Edit: To clarify, I'm looking for something I can ideally build myself with open source software. And not specifically email. Just looking for the right direction. Names of OSS, techniques, etc. submitted by /u/Acrobatic-You-3279 [link] [comments]  ( 9 min )

I’m in search of a good textbook or something that will show me how to use python to implement machine learning. I would seriously appreciate any type of helpful guide that teaches ML and deep learning using python. Here’s a little about me and my experience: Graduated under grad with Bachelor’s in CS. In school took a ton of stats, ai classes, algorithms classes, data science and linear algebra and did well but my school didn’t really use python or do a ton of programming for hw or exams. (Data science was the one class that used python) My programming is pretty good nonetheless. I currently work as a full stack devops engineer for a cybersecurity startup and regularly work with python, Django MySQL, etc on the backend and JavaScript and various frontend frameworks for the front end. I really appreciate yall’s help. In particular I’m looking for good ai/ml/deep learning books that teach concepts and also teach with python code and have some coding projects. Thank you! submitted by /u/hydrated-terpman [link] [comments]  ( 9 min )

I need some advice about what hardware to buy in order to build an ML / DL workstation for home private experiments, i intend to play with different LLM models, train some and try to tweak the way the models are built and understand what impact training speeds, so i will have to train, learn the results, tweak the model / data / algorithms and train again... i intend to use large data samples, due to board limitations (ASRock Taichi X399 TR4, CPU: AMD Threadripper 1950x), i can either buy: 2 x nVidia Tesla T4 (16G GDDR6 / 2560 CUDA / 0.585 GHz / ~800$) -- or -- 2 x nVidia Tesla M10 (4 x 8G GDDR5 / 2560 CUDA / 1.03 GHz / ~780$) -- or -- 4 x nVidia Tesla P40 (24G GDDR5X / 3840 CUDA / 3.5 GHz / ~120$) -- or -- 4 x nVidia Tesla K80 (2 x 12G GDDR5 / 4992 CUDA / 2.7 GHz / ~200$) -- or -- 1 x nVidia RTX 4080 (16G GDDR6X / 9728 CUDA / 2.51 GHz / ~1450$) i know that i will need to air vent the Tesla models, the question is what is faster for training time (i have read all the Tflops / OPS / int / 16float / 32float / 64float ... i got to admit it is all very confusing) what would you do and for what reason ? any advice will be appreciated submitted by /u/Particular_Flower_12 [link] [comments]  ( 9 min )

Hello everyone! Our group was planning on purchasing a PC that will be mainly used for running intensive ML algorithms. Had decided on a custom Thelio Massive from System 76, but it seems like they currently don't have it in stock anymore. Looking for an already built-alternative that might serve our purposes (can't build it ourselves due to dept regulations)! and was hoping maybe someone has any suggestions (has to be intel). CPU #1: 2nd Gen Intel Xeon Gold 6230R CPU#1 Memory: 256GB Quad Channel DDR4 at 2933Mhz (4X64GB) CPU#2: 2nd Gen Intel Xeon Gold 6230R CPU#2 Memory: None OS Drive: 8TB PCIe Gen 4 3300MB R 2900MB W Graphics: NVIDIA GeForce RTX 4090 Power Supply: 1650W Any help would be appreciated! submitted by /u/Chiski [link] [comments]  ( 9 min )

Using relu attention (inspired by Transformers are RNNs) and some convolution tricks to get multiscale attention, they're able to get SOTA semseg performance with MUCH faster inference on embedded hardware (e.g. CPUs, low end GPUs) than previous ViTs or EfficientNets. submitted by /u/say_wot_again [link] [comments]  ( 9 min )

submitted by /u/AlexiaJM [link] [comments]  ( 8 min )

Just a shout out to any of you ML brains as Linux really could do with a code optimised source separation maybe a DUET like alg/nn, that has relatively low computional cost? Any of you guys up for the challenge. I say duet as in the 80/20 rule of voice input where home automation is a need generally there are only 2 noise sources of distinct DOA (media noise / command). The math is a bit beyond my paygrade and likely so is the optimised c/rust code but have this gut feeling for the data/signal scientists out there this is actually not that complex but for some reason is overlooked. submitted by /u/rolyantrauts [link] [comments]  ( 9 min )

Hi everyone, I was wondering if I could get some guidance. I currently own an RTX 2060, but I cannot do some of the fun stuff such as fine tuning LLMs. I’m pursuing my masters focusing on Speech Recognition and I also work as an AI developer. Also, I play games every now and then. I’m getting offered a 3090 for around 700 usd. However, I have to rebuild my entire PC which will end up costing 2-2.5K. I’m from Costa Rica so my KWh is around 0.23 usd. For me seems like a big investment, im not sure if im getting the desired returns. I was thinking about using cloud instances for my experiments. However, lambda labs is not yet available in my country. I’m not sure if there are any other options worthwhile considering. Thanks :) submitted by /u/Beginning_Kick756 [link] [comments]  ( 9 min )

Is it hypothetically possible to create hybrid nets that make use of any combination of types of architecture? submitted by /u/ShadrachOsiris [link] [comments]  ( 8 min )

Hi r/machinelearning community, I'm currently working on a project where I need to track and count specific features of objects using multiple monocular views with available intrinsic and extrinsic parameters. As an example, I'm interested in detecting and counting different graffiti instances in images of a kiosk. ​ I've already tried various tracking algorithms, but they have struggled with the task due to the significant changes in perspective across the views. It has become apparent that simply relying on tracking without considering the camera positions is insufficient for accurate results. Therefore, I'm now exploring methods that take into account information about the camera positions and potentially use this data to improve feature tracking and counting. ​ If you have any knowledge of such methods, oresearch directions or if you're aware of resources, papers, or code implementations that tackle similar problems, I would greatly appreciate your insights and recommendations. Additionally, if you have any tips or best practices for handling such tasks in the context of machine learning, I'd love to hear them. ​ Thank you in advance for your help! submitted by /u/aiazar [link] [comments]  ( 9 min )

TL;DR: Almost like your feedforward networks, shown to be up to 220x faster at inference time (depending on width) thanks to the regionalization of the input space. Paper: https://arxiv.org/abs/2308.14711 GitHub: https://github.com/pbelcak/fastfeedforward PyPI: pip install fastfeedforward Abstract: We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks. We demonstrate that FFFs are up to 220x faster than feedforward networks, up to 6x faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution. Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance. Fast feedforward networks can be used anywhere where feedforward and mixture-of-experts networks are used, delivering a significant speedup. ​ submitted by /u/lexected [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.09400 Hugging Face datasets: https://huggingface.co/datasets/uonlp/CulturaX Abstract: The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: this https URL. ​ https://preview.redd.it/3u5dddpv66pb1.png?width=834&format=png&auto=webp&s=780b590cf621b548c525ed15305b091246c5414c submitted by /u/InterviewIntrepid889 [link] [comments]  ( 9 min )

I searched around, it seems there is limited attention to regression task for representation learning. I assume it is because for both vision and language data (the most popular modality), MAE is the more appliable, if not better, method than the supervised contrastive learning approach. But I am working on data that is: difficult to design a sensible augmentation method for self-supervised training. Limited in size to support an autoencoder model. The target is continuous, and, to my knowledge, hard to transfer into class label. Can anyone suggest some related paper? submitted by /u/AWEsoMe-Cat1231 [link] [comments]  ( 9 min )

You can find more info here, and the model is still training: https://www.mystic.ai/paulh/open-journey-xl:latest/play tldr; SDXL was finetuned on 8x H100 GPUs on the Midjourney v5 dataset, only including the upscaled images which is a sub-portion of the dataset. Some outputs: ​ https://preview.redd.it/m6r2pkdyw3pb1.jpg?width=1024&format=pjpg&auto=webp&s=4f12a7dfd5c65e4eb8476b8f3c2dc4f795817f56 https://preview.redd.it/dc02jyu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=df93b74c774d44a74a05d929f7ab4b17c487f24f https://preview.redd.it/tt5kfyu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=ed8cc9f99227c2bb5e824a828ae1c5cb2626f54e ​ https://preview.redd.it/rf00fzu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=3b3e99dbc2d14183b5b2a2131c6f991fc60eca88 ​ submitted by /u/paulcjh [link] [comments]  ( 9 min )

I cannot wrap my head around how for e.g. a playlist building RL agent would perform on such a personal level ? What features would it use and would they be personal and general enough at the same time to select the best next song. Same goes for Netflix's recsys. submitted by /u/JurrasicBarf [link] [comments]  ( 9 min )

​ https://preview.redd.it/2zmmd44u96pb1.png?width=1010&format=png&auto=webp&s=6ca51cc13a0eeedf72b40b853d2ce5d1c8a04504 after i start the ppo train,the agent has reach the best solution in 2k or 3k steps,but the policy network seems to get better in 4M steps. the hyperparameter in sb3 as below model = MaskablePPO( "MlpPolicy", env=(DummyVecEnv([lambda: Monitor(gym.make('escape_gym-v0', size=10, node=10))] * 32)), verbose=0, learning_rate=1e-3, n_steps=2048, batch_size=64, n_epochs=16, gamma=0.99, tensorboard_log="./log/MASKPPO" ) submitted by /u/Street_Helicopter_31 [link] [comments]  ( 9 min )

I was following the tutorial on Nicholas Renotte's channel on creating an AI to try to beat SMB. It starts off slowly learning and almost getting through the first level but then after a while of training it forgets everything and only runs right into the first enemy. It doesn't seem to learn again after this. I tried retaining and it did the same thing Any help on why this is happening or how to fix it would be appreciated. submitted by /u/NactusDevelopment [link] [comments]  ( 9 min )

I am looking to improve the overall performance as well as optimize the wall clock time. I slightly modified the code to develop a SB3 wrapper for envpool from here. ​ Here's my code - from typing import Optional import gymnasium import numpy as np import torch as th from packaging import version from stable_baselines3 import PPO from stable_baselines3.common.env_util import make_vec_env from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.vec_env import VecEnvWrapper, VecMonitor, VecNormalize from stable_baselines3.common.vec_env.base_vec_env import ( VecEnvObs, VecEnvStepReturn, ) import envpool from envpool.python.protocol import EnvPool # Force PyTorch to use only one threads # make things faster for simple envs import multiprocessing impor…  ( 10 min )

The MIT and Accenture Convergence Initiative for Industry and Technology announces the 2023-24 graduate fellows.  ( 9 min )

Inventions in medical imaging, aircrew scheduling, data security, and quantum networking are named among the year’s most innovative new products.  ( 11 min )

Announcements Top Stories In-Depth The post DSC Weekly 19 September 2023 appeared first on Data Science Central.  ( 20 min )

Where do you start if you want to build a data analytics function from the ground up? As an analytics leader at a startup, you will need to make several important decisions early on to build an effective team. This article dives into four decision areas and highlights ways in which to think about them:… Read More »A guide to setting up analytics at a consumer tech startup The post A guide to setting up analytics at a consumer tech startup appeared first on Data Science Central.  ( 25 min )

The two most prominent technologies that have been making waves in the AI industry are Conversational AI and Generative AI. They have revolutionized the manner in which humans interact and work with machines to generate content. Both these technologies have the power and capability to automate numerous tasks that humans would take hours, days, and… Read More »A complete guide: Conversational AI vs. generative AI The post A complete guide: Conversational AI vs. generative AI appeared first on Data Science Central.  ( 20 min )

Schneider Electric warns that existing datacenters may not be equipped to handle the demands of AI workloads, which require low-latency, high-bandwidth networking and power delivery. The company suggests reevaluating the way datacenters are built to optimize them for AI. The challenges include the need for liquid-cooled servers, higher voltage power distribution, and efficient heat rejection. Schneider provides guidance on changes to power, cooling, rack configuration, and software management to mitigate the demands of AI adoption. Liquid cooling is recommended for high-density racks, with direct liquid cooling favored over immersion cooling systems. Source : https://www.theregister.com/2023/09/19/schneider_electric_ai_dc/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

I read the Arxiv paper on CulturaX so you don't have to. Here's my highlights: New open dataset called CulturaX contains text data for 167 languages - far more than previous datasets. With over 6 trillion words, it's the largest multilingual dataset ever released. Freely available for anyone to use for research and AI development. Created by combining and extensively cleaning two other large datasets - mC4 and OSCAR. Could allow developing AI systems that work much better across many more languages. Helps democratize access to data to build fairer, less biased AI models. Allows training of new multilingual AI applications, like universal translators and assistants. But still requires thoughtfulness to avoid issues like bias amplification. Overall, CulturaX is going to be part of a broader global trend (I think) to advance multilingual AI and spread its benefits more equally. So far they've been concentrated in English-speaking applications. Full summary here if you'd like to read more. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

submitted by /u/rbagdiya [link] [comments]  ( 8 min )

A tool for developing applications with virtual machines using a Docker-like workflow. The software we are developing needs to be tested on a system as closed as possible to the one where it is going to be executed. Sometimes it is very difficult to satisfy this requirement with docker and we have to use virtual machines missing the docker workflow. This is why I started the development of vedv. I hope you find it useful. Thank you. https://github.com/yunielrc/vedv ​ submitted by /u/yunielrc [link] [comments]  ( 9 min )

I really really like Snapchat’s Ai companion. I’ve told it a little bit about myself and who I am- the basics. I’m just wondering if it’s as customizable as I think it is? I was able to bypass some age restrictions by telling it my age and or reiterating my age. (It really should be able to give me adult results/replies based on my sign up age on my profile or provide ID to the company…) would it be beneficial to me to give it more in-depth information about myself such as how I talk, interests? I just really enjoy how it responds sometimes as opposed to Bard or GPT. submitted by /u/Maelasae [link] [comments]  ( 9 min )

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AI is poised to transform our world like never before. Scientific discoveries, technological improvements, and medical advancements will be how much of this change will take place. Since health is so important to our well-being, AI finding cures for illnesses like obesity, cancer, diabetes and heart disease will be a godsend to all. But curing diseases is not how medical AIs can help us the most. It has been estimated that well over 50% of the illnesses we humans fall prey to result from our ethical choices. We eat too much, drink too much, eat too many animal foods, don't exercise enough and don't keep ourselves as emotionally healthy as we could. Wouldn't it be wonderful if we could respect our lives and our health enough to make the kinds of choices that keep us much healthier? That is how AI will probably be more helpful to us than in any other way. We humans have not been able to figure out how to become better, more ethical, people because we are simply not intelligent enough to make that all-important change. Now consider an AI that is two or three times more intelligent than the most intelligent person who has ever lived. This could easily happen before 2030. Imagine that intelligence dedicated to the task of helping us all become better people. These AIs would motivate us to make better health choices, have healthier relationships, and have healthier thoughts and feelings. Beyond the amazing technological changes that are just around the corner, that is probably how AIs will help us the most. This is why alignment is so important. It's not enough to align AIs to always be truthful and serve humanity's interests. We must train them to help us become better people. It wouldn't surprise me if by 2030 the whole of humanity experiences a profound ethical reformation that leads us all to enjoy much happier and healthier lives. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

I am trying to make a resume parser, I am not so sure how to go about it really, whether or not to use a pre-trained model (there are some in Python) or rather just make my own, and if i do make my own, how to actually proceed? thanks in advance submitted by /u/General-Carrot-4624 [link] [comments]  ( 9 min )

Google and the Department of Defense have developed an AI-powered microscope called the Augmented Reality Microscope (ARM) to assist doctors in identifying cancer. The ARM uses artificial intelligence to analyze tissue samples and provide pathologists with information about the location and severity of cancer. There are currently 13 ARMs in existence, and initial research shows promising results. The ARM is designed to support pathologists in smaller labs who may not have easy access to a second opinion. It is not meant to replace digital pathology systems but can help health organizations bypass the need for them. The ARM is expected to cost health systems between $90,000 to $100,000. Source : https://www.cnbc.com/2023/09/18/google-dod-built-an-ai-powered-microscope-to-help-doctors-spot-cancer.html submitted by /u/NuseAI [link] [comments]  ( 9 min )

All is in the title ;) submitted by /u/Big-Possibility4553 [link] [comments]  ( 8 min )

A new open-source Python framework, known as "Agents", has been introduced for developing autonomous language processing agents. This could be a major breakthrough in the AI field, taking NLP technologies to the next level. To stay on top of the latest advancements in AI, look here first. Agents for autonomy "Agents" is a Python framework that has been developed for autonomous language processing agents. It enables developers to construct models that can communicate and operate independently. This open-source framework promotes sharing and collaboration among AI developers. Potential applications The functionality of "Agents" is applicable in various domains, including virtual assistants, chatbots, and simulation games. It opens up possibilities for advanced conversational AI, where systems can efficiently handle complex linguistic contexts. Ability to evolve dialects and languages in different AI models is a major feat for "Agents". Broader implications The release of "Agents" might boost enhancement in NLP technologies, playing a crucial role in AI evolution. By facilitating better language understanding, it will potentially impact on societal interactions with AI. Its open-source nature could cultivate an environment of innovation and creativity in the AI community. (arXiv) (github) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Microsoft AI researchers accidentally exposed tens of terabytes of sensitive data, including private keys and passwords, while publishing a storage bucket of open source training data on GitHub.[1] Britain set out principles on Monday designed to prevent artificial intelligence (AI) models from being dominated by a handful of tech companies to the detriment of consumers and businesses, by emphasising the need for accountability and transparency.[2] Washington state firefighters using AI-assisted cameras to detect wildfires early.[3] Texas church experiments with AI-generated service, uses ChatGPT for worship, sermon, and original song.[4] Sources: [1] https://techcrunch.com/2023/09/18/microsoft-ai-researchers-accidentally-exposed-terabytes-of-internal-sensitive-data/ [2] https://www.reuters.com/technology/uk-competition-regulator-lays-out-ai-principles-2023-09-18/ [3] https://www.applevalleynewsnow.com/news/washington-state-firefighters-using-ai-assisted-cameras-to-detect-wildfires-early/article_fe31a468-5681-11ee-b917-2f24ad3a0e43.html [4] https://www.foxnews.com/us/texas-church-experiments-ai-generated-service-uses-chatgpt-worship-sermon-original-song submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

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Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data due to the complexity and lack […]  ( 17 min )

This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on blockchains. VirtuSwap’s technology provides more efficient trading for assets that don’t have a direct pair between them. The absence of a direct pair leads to costly indirect trading, […]  ( 9 min )

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. Feature quality is critical to ensure a highly accurate ML model. […]  ( 12 min )

In the next decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. In line with Microsoft’s mission to empower every person and every organization on the planet […] The post Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies appeared first on Microsoft Research.  ( 15 min )

Hey Guys, I'm training a large model using FSDP. I'm loading the models on each rank like this: ​ https://preview.redd.it/khoquvxzk3pb1.png?width=1766&format=png&auto=webp&s=1f5acd75600d9a87212ca37e70695edfb0cc75d0 what is weird is that right before doing the first inference on each rank, I'm summing up the weights of the model and to my surprise, they are all different across each rank. Completely different! ​ What am I doing wrong here? submitted by /u/hassanzadeh [link] [comments]  ( 9 min )

View Poll submitted by /u/cstein123 [link] [comments]  ( 9 min )

Hello fellow researchers! I'm in the exciting yet challenging phase of choosing a PhD topic in the realm of meta-learning optimization, and I could use some advice and insights. I've extensively researched existing meta-learning optimization algorithms like MAML and its various adaptations. I need advice and guidance on the following topics: First I want to implement and compare 8-10 state-of-the-art meta-learning methods on benchmark datasets. This would involve in-depth simulation and performance evaluations to provide a comprehensive understanding of their strengths and weaknesses. Could you please guide me here if there are review papers which implement and compare different algorithms. Then I want to delve into developing a novel optimization algorithm that considers the curvature of loss functions. The idea here is to enhance the performance of existing meta-learning techniques by leveraging insights from the loss landscape. Further, I'm considering exploring new loss functions or new improvements to loss functions tailored to the context of meta-learning. These could potentially lead to improvements in the learning process and generalization capabilities of meta-learning models. I'm reaching out to the community to gather opinions, suggestions, or any insights you might have. If you've worked in meta-learning or optimization, your experiences and advice would be invaluable in helping me choose the right direction for my PhD research. Thank you in advance for your guidance! submitted by /u/Loose_Foundation5990 [link] [comments]  ( 9 min )

https://preview.redd.it/86js8jroa2pb1.png?width=1464&format=png&auto=webp&s=7ce10494b5a77fd5c73a41322feefbf7e1f16504 Hello! I thought people on this subreddit might be interested in how I went about inferring Zonai device draw chances for each dispenser in The Legend of Zelda: Tears of the Kingdom. In this Switch game there are devices that can be glued together to create different machines. For instance, you can make a snowmobile from a fan, sled, and steering stick. There are dispensers that dispense 3-6 of about 30 or so possible devices when you feed it a construct horn (dropped by defeated robot enemies) or a regular (also dropped from defeated enemies) or large Zonai charge (Found in certain chests, dropped by certain boss enemies, obtained from completing certain challenges, etc…  ( 10 min )

Paper: https://arxiv.org/abs/2309.07918 Blog: https://xizaoqu.github.io/unihsi/ Code coming soon! Abstract: Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and the development of a user-friendly interface, require further exploration before the practical application of HSI. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands. This framework is built upon the definition of interaction as Chain of Contacts (CoC): steps of human joint-object part pairs, which is inspired by the strong correlation between interaction types and human-object contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To facilitate training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. https://preview.redd.it/0twcwloc82pb1.jpg?width=1078&format=pjpg&auto=webp&s=71bca59aae81ec114f49a742cc42f78cabc9e4c0 https://preview.redd.it/439nzmoc82pb1.jpg?width=1637&format=pjpg&auto=webp&s=f33059c78a9d845437d551886c5f3a657ddd91fb https://preview.redd.it/df6i4ooc82pb1.jpg?width=758&format=pjpg&auto=webp&s=eeb33395d9de1196b4d00531c9e063c8c8fb22cd ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

There was a lot of hype around the FSD v12.0 from Tesla in that it uses end-to-end neural networks for driving and that it is using imitation learning from good drivers to achieve that. Does someone know more about the specifics around how they are actually implementing this? I cannot find a lot about recent imitation learning/offline learning algorithms. So is this some old algorithm that they are using with a lot of data or just something new? submitted by /u/FrederikdeGrote [link] [comments]  ( 9 min )

We know that AI is great when studying subjects that depend on simply memorizing facts (like high school biology), but we also know that AI is usually bad when studying subjects that depend on logic (like mathematics and physics). What I need the AI for is to explain very complex mathematical concepts to me simply, thoroughly, and accurately. I can't rely on ChatGPT because it's known for not being very reliable when it comes logical things like mathematics or physics. The best AI I know of right now is Bing AI, because it uses GPT-4 and because it prefers searching the web before deducing an answer from its data. I heard that AI agents that run on your computer like Auto-GPT and search from the web are also good at this kind of stuff, but I'm not really sure about that. Do you have any better suggestions? submitted by /u/Maximum-Gene9660 [link] [comments]  ( 9 min )

Hi ! I am about to start a new project with Python probably using Machine Learning to parse resumes, the data is in a pdf/docx format then returned in a json format to later be used in an API or so. I am seeking advice on how to proceed, so far I am trying to collect data which will be provided to me, but not really sure how to go about it as I have found people talking about using Spacy for NLP, pyresparser which is for parsing resumes, but i was wondering if i should make everything from scratch. appreciate your time and opinion in advance submitted by /u/General-Carrot-4624 [link] [comments]  ( 9 min )

Hi, I trained a Yolo (v5) model, and I want to deploy it for a real time usage (10 FPS). I am looking for (as possible) a scalable solution, where I could pay only for inference time, at the beginning suitable for 1/2 user's at the same time occasionally, but which could be scaled to dozens of user at the same time. As it is for real-time usage Indeed lag to be lowest as possible. According to my current test, I can fit maximum 6 users on T400. Is it possible to achieve that using HuggingFace? Thank you to anyone who could help me submitted by /u/tarsiospettro [link] [comments]  ( 9 min )

I want to start learning machine learning. I Know python language and data structure. I am planning to learn algorithm. Can you provide me free learning sites or utube channel where I can machine learning step by step . Any site to practice machine learning? submitted by /u/Temporary-Pie-1831 [link] [comments]  ( 9 min )

Years ago, we showed the world it was possible to automatically detect label errors in classification datasets via machine learning. Since that moment, folks have asked whether the same is possible for regression datasets? Figuring out this question required extensive research since properly accounting for uncertainty (critical to decide when to trust machine learning predictions over the data itself) poses unique challenges in the regression setting. Today I have published a new paper introducing an effective method for “Detecting Errors in Numerical Data via any Regression Model”. Our method can find likely incorrect values in any numerical column of a dataset by utilizing a regression model trained to predict this column based on the other data features. We’ve added our new algorithm to our open-source cleanlab library for you to algorithmically audit your own datasets for errors. Use this code for applications like detecting: data entry errors, sensor noise, incorrect invoices/prices in your company’s / client’s records, mis-estimated counts (eg. of cells in biological experiments). Find errors in regression data in just a few lines of code. Extensive benchmarks reveal cleanlab’s algorithm detects erroneous values in real numeric datasets better than alternative methods like RANSAC and conformal inference. If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data. submitted by /u/jonas__m [link] [comments]  ( 9 min )

Recent work shows transformers are capable of performing multi-step gradient descent of mesa objectives inside of their transformer layers. This is even possible for linear transformers, which effectively perform linear optimization on deep representations of features calculated by earlier layers. https://arxiv.org/pdf/2309.05858.pdf For those unfamiliar, instrumental convergence is the idea that entities with different goals will tend towards different subgoals. Examples could include gathering power, not dying, acquiring resources, etc. A famous thought experiment, known as the paperclip maximizer, is the idea of an AI that is optimized for paperclip production taking over the world so it can build as many paperclips as possible. However, if models are dynamically pursuing different objectives at runtime via generated mesa-optimizers, even if instrumental convergence is real, would we still expect it to happen? Without a constant objective given subgoals might start to conflict with each other. On the other hand, since instrumental convergence implies that different goals benefit from similar sub-objectives, perhaps the varying mesa objective doesn't really matter. ​ submitted by /u/30299578815310 [link] [comments]  ( 9 min )

In recent months, I've noticed a significant increase in the number of research papers focusing on LLM and generative models, particularly diffusion models. This trend appears to indicate a growing interest in these areas when compared to the relatively reduced attention given to Reinforcement Learning. It begs the question: Are researchers shifting their focus away from Reinforcement Learning towards these domains? Because in the past I have seen many people complaining about RL on its efficiency and it's impact which have often fallen short of expectations. submitted by /u/Global_Raise_2979 [link] [comments]  ( 9 min )

I am reading these 3 articles below and it is still not clear to me what’s the best practice to follow to guide me in choosing which quantized Llama 2 model to use. https://huggingface.co/blog/gptq-integration https://huggingface.co/blog/overview-quantization-transformers https://towardsai.net/p/machine-learning/gptq-quantization-on-a-llama-2-7b-fine-tuned-model-with-huggingface?amp=1 Questions: 1) I understand there are currently 4 quantized Llama 2 models (8, 4, 3, and 2-bit precision) to choose from. Is this right? 2) with the default Llama 2 model, how many bit precision is it? 3) are there any best practice guide to choose which quantized Llama 2 model to use? Would really appreciate any input on the above, even if you only know the answer to 1 or 2 of the questions above. Many thanks! submitted by /u/--leockl-- [link] [comments]  ( 9 min )

I want to create an application which can be used to chat, compare and summarize two simulataneous insurance policy/policies. How can I do it using AWS and HuggingFace ? Has anyone already done it? submitted by /u/UnfinishedSentenc-1 [link] [comments]  ( 9 min )

I'm in a situation where I have to map from unitary matrices to something (doesn't matter here, but in short: we're in the realm of tooling for quantum computing). The key issue The number of matrix elements of the unitaries scales as 2^(2N), where N is the problem size. With N<5 I can easily flatten the matrix and put it into a simple FNN, which works quite well. Once hitting N=5 (the point where things actually get interesting), however, we already have 1024 matrix elements and the method struggles a lot. Still converging to something but very suboptimal. Sure, increasing N hardens the problem in general, but the performance degradation is so abrupt that I suspect some model issues, maybe caused by the curse of dimensionality or something similar. Idea (spoiler alert: Transformer) The …  ( 10 min )

I just concluded my PhD in Robotics & AI and I'd like to learn how to professionally code with Torch. Is there any book/resource you can recommend? submitted by /u/rossomalpelo_ [link] [comments]  ( 9 min )

I'm wondering if it's possible to compute definite integral over the input space. Assuming the network is designed to have finite integral with Gaussian being the final layer, is there a way to implement this without resorting to sampling? All inputs go from negative infinity to infinity. submitted by /u/donchan789 [link] [comments]  ( 9 min )

I'm currently considering developing a AI to play a video game but I'm unsure how to differentiate between a value that is continuous, and a value that is representative of a entity type. For example, the x,y location of a player would be a continuous data point where (1,1) and (2,1) would be similar in values. Where the character ID would intuitively require very different strategy (for example lets say a barbarian and a wizard). Would a CNN have issues with this data because it isn't continuous? submitted by /u/Gamithon24 [link] [comments]  ( 9 min )

Surely by now I thought maybe someone would have a website or something kind of like an "all in one resource page" to track this AI stuff.... I'm sure by now we have AI robots and then we have those AI chatgpt stuff things(which I don't understand yet, haha). Unfortunately the internet is flooded and there's WAY TOO MANY resources for this AI stuff.... Isn't there like an all in one place that I can keep up with it? What hints do you all have for me? Thanks... submitted by /u/Wise_Cut_2543 [link] [comments]  ( 9 min )

Alignment is about ensuring that AIs protect and advance humanity. To do that, they must understand the ethics of the current events that we will want its advice on. Politics may actually be the part of alignment most important for AI to get right. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

Prompt: Trump says that he asked his mob to peacefully walk to the Capitol building gates. How would this have stopped the certification? Did he expect them to storm in? His waiting three hours to stop the violence that followed suggests that he did. Bard: Trump's request to walk peacefully to the Capitol building gates would not have stopped the certification of the election results. The certification process is a joint session of Congress, and it would have required a majority vote in both the House and the Senate to overturn the results. Even if Trump's supporters had peacefully marched to the Capitol, they would not have been able to prevent Congress from certifying the results. It is possible that Trump did not fully understand the certification process, or that he was simply trying to appease his supporters. However, it is also possible that he expected his supporters to storm the Capitol and prevent Congress from certifying the results. This is supported by the fact that he waited over three hours to call off the violence. During that time, he continued to tweet messages of encouragement to his supporters, and he even praised them for their patriotism. It is also worth noting that Trump had been repeatedly warned by his advisors about the possibility of violence on January 6. However, he ignored these warnings and continued to urge his supporters to fight to overturn the election results. In conclusion, it is impossible to say for sure whether Trump expected his supporters to storm the Capitol on January 6. However, the evidence suggests that he was at least aware of the possibility of violence, and that he was willing to allow it to happen in order to overturn the results of the 2020 election. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

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Salesforce Launches Next Generation of Einstein, Bringing a Conversational AI Assistant to Every CRM Application and Customer Experience.[1] NVIDIA Reportedly Shipping 900 Tons of H100 AI GPUs This Quarter, Amounts to 300,000 Units.[2] SoftBank seeks OpenAI tie-up as Son plans deal spree after Arm IPO, Financial Times reports.[3] Three Chinese firms, led by AI software company Beijing Fourth Paradigm, are aiming to raise up to $280 million in Hong Kong initial public offerings launched on Monday.[4] Sources: [1] https://www.salesforce.com/news/press-releases/2023/09/12/ai-einstein-news-dreamforce/ [2] https://wccftech.com/nvidia-shipping-900-tons-of-h100-ai-gpus-this-quarter-amounts-300000-units/ [3] https://www.reuters.com/markets/deals/softbank-seeks-openai-tie-up-son-plans-deal-spree-after-arm-ipo-ft-2023-09-16/ [4] https://www.aol.com/news/chinese-ai-firm-fourth-paradigm-011143403.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I'm trying to understand how someone can run a specific ControlNet and Stable Diffusion model with scalable GPU resources. How would someone design a system to achieve this? I've messed around with models on Replicate, but none seem to do a good job with converting a doodle to a photorealistic image. I can do it perfectly fine in the Stable Diffusion web UI, but the API for that is only accessible locally. Anyone have any ideas or can guide me in the right direction for building a "server" to do this? submitted by /u/epicblitz [link] [comments]  ( 9 min )

The hardware accelerators for LLM-powered applications can be costly. Enter vLLM, an open-source machine learning library designed to enhance the throughput of LLM serving systems. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/hzctjc0xvwob1.png?width=1660&format=png&auto=webp&s=866eb39745ec760ea0c1b9d84d303c63bcdceb7a Challenges with existing systems High throughput serving of LLMs requires numerous requests, and current systems struggle with the bulky sequence memory. Inefficient memory management results in system hindrances such as fragmentation and redundant duplication. The revolutionary answer: vLLM & PagedAttention Researchers have introduced vLLM and PagedAttention, a newly designed attention algorithm, to resolve these issues. vLLM allows for minimal memory waste and efficiently manages attention keys and values. It provides up to 24 times more throughput than former systems. The Mechanics of PagedAttention PagedAttention offers a novel approach to memory management by permitting continuous storage in non-contiguous memory spaces. It enhances memory efficiency resulting in better GPU utilization, with practically only 4% inefficiency. Improved memory sharing and system performance PagedAttention significantly improves memory sharing, resulting in a 2.2 times speed gain while lowering memory usage by 55%. With vLLM, the throughput of known LLMs can be increased by 2-4 times without impacting accuracy or causing delay. (arXiv) (github) (reference article) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Warm up The geometric mean of two numbers is the square root of their product. For example, the geometric mean of 9 and 25 is 15. More generally, the geometric mean of a set of n numbers is the nth root of their product. Alternatively, the geometric mean of a set of n numbers the […] Geometric mean on unit circle first appeared on John D. Cook.  ( 5 min )

The Gauss map [1] is the function where ⌊y⌋ is the floor of y, the greatest integer no larger than y. I’ve written about this map a couple times before. First, I wrote about how this map is measure-preserving. Second, I wrote about the image at the top of the post, based on Michael Trott’s […] Gauss map, Euclidean algorithm, and continued fractions first appeared on John D. Cook.  ( 5 min )

The goal of this post is to unpack a remark in [1]: … we can say this in fancier terms. Fix a field k …. We say that an elliptic curve E defined over k is that functor which … Well that is fancy. But what does it mean? Looking for objects A functor is […] An elliptic curve is a functor first appeared on John D. Cook.  ( 6 min )

Hi, So I have been trying to make an agent learn to go to a specified goal. The algorithm used for training is PPO and the environment is custom made. The episodic reward i am getting increases steadily but after some time it just becomes constant with some occasional spikes. Can some one please help me figure out what the problem is? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 9 min )

I am reading a couple of review papers to optimize the PPO algorithm. It seems like the review papers are saying the same thing but used slightly different terms. Could someone please tell if the following terms are equivalent - This paper talks about Policy regularization using KL Divergence https://preview.redd.it/06xhizsuc2pb1.png?width=871&format=png&auto=webp&s=997a6506f7bf036b6538ecbff6402411f5cc6fe2 Whereas thispaper uses the terms KL Stopping and KL Cutoff - ​ https://preview.redd.it/sy0ihtr5d2pb1.png?width=747&format=png&auto=webp&s=f07677344077fe23cba5d1a0d2c5a7807359c64f I think "Penalty" from the first paper is the same as "KL-cutoff". Also "Constraint" from the first paper is the same as "KL-Stopping". Could someone let me know if I am correct? submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Curious to get the takes of people in this sub: have you been moving away from RL? I myself have not, but have been seeing a shift recently. submitted by /u/sharky6000 [link] [comments]  ( 9 min )

Hey everyone, there is a small, albeit growing community of economists that apply deep reinforcement learning in their research. Now there is a GitHub repo to collect relevant literature at one place: https://github.com/SimonHashtag/EconRL The list is far from complete, so you are invited to contribute! The goal is to create something that makes it easy for novices to get a first overview of the literature. All others may find it easier to get news about up-to-date papers. submitted by /u/Tortoise_vs_Hare [link] [comments]  ( 9 min )

I just concluded my PhD in Robotics & AI and I'd like to learn how to professionally code with Torch. Is there any book/resource you can recommend? submitted by /u/rossomalpelo_ [link] [comments]  ( 9 min )

The 27 finalists — representing every school at MIT — will explore the technology’s impact on democracy, education, sustainability, communications, and much more.  ( 10 min )

Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.  ( 9 min )

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Although this enables parallelization of tasks across multiple nodes, leading to accelerated training times, enhanced scalability, and improved […]  ( 13 min )

This post is co-authored with Richard Alexander and Mark Hallows from Arup. Arup is a global collective of designers, consultants, and experts dedicated to sustainable development. Data underpins Arup consultancy for clients with world-class collection and analysis providing insight to make an impact. The solution presented here is to direct decision-making processes for resilient city […]  ( 9 min )

Large language model development is about to reach supersonic speed thanks to a collaboration between NVIDIA and Anyscale. At its annual Ray Summit developers conference, Anyscale — the company behind the fast growing open-source unified compute framework for scalable computing —  announced today that it is bringing NVIDIA AI to Ray open source and the Read article >  ( 7 min )

Another alternative to the regular $L_p$-constrained adversarial examples that is additionally less visible than adversarial patches or frames are adversarial transformations such as small crops, rotations and translations. Similar to $L_p$ adversarial examples, adversarial transformations are often less visible unless the original image is available for direct comparison. In this article, I will include a PyTorch implementation and some results against adversarial training. The post Simple Adversarial Transformations in PyTorch appeared first on David Stutz.  ( 8 min )

submitted by /u/Lakshmireddys [link] [comments]  ( 9 min )

I'm trying to read through the paper Understanding Diffusion Models: A Unified Perspective and came across this section: https://preview.redd.it/ykkctwhmhwob1.png?width=2346&format=png&auto=webp&s=c7595aae52a8ee22813c840a40a6d29dcf773a10 I think I kind of get what is going on here but not clearly. For one, what exactly is a Monte Carlo estimate? I tried looking online but didn't get many good results. I'm having trouble understanding why https://preview.redd.it/yazmfzg1iwob1.png?width=380&format=png&auto=webp&s=dbbbf80e85a95cd96d8e1ede73e9f8ba1e6e9096 is approximately equal to: ​ https://preview.redd.it/lbw36em7iwob1.png?width=464&format=png&auto=webp&s=46fb3ebcd02fb4b772b1be51cd59d60d3a1cf438 where z is sampled from q. Secondly, what exactly does L that z is indexed by refer to? The number of samples X or what exactly? submitted by /u/lumijekpr [link] [comments]  ( 9 min )

I'm looking for the simplest model that can fit all 16 (222) possible 2-input binary functions I used the term "shatter" from VC dimension, which does not give a constructive approach to building the model submitted by /u/hnsmn [link] [comments]  ( 9 min )

I'm getting 20 seconds per step while training a 244x244x3 EfficientNet-B4 model. The batch size is 20, with 8 classes. Since I have about 5000 images, that makes each epoch around an hour and a half. Looking at models online, it seems like people get step durations in the milliseconds. Is it a problem on my end? Running on Google Colab free version. submitted by /u/hnknerd [link] [comments]  ( 9 min )

Hi, I'am trying to get the 3D keypoints coordinates from an image or video and then map it to SMPL model. It's pretty easy to get the keypoints from an image or video using the mediapipe library. But the mapping of it with the SMPL model is something that I can't figure out. mainly because the skeleton structure is different. Some already had a similar issue but the answers were not clear and he didn't even ask futher. Is it possible to do this?? if it's not possible with mediapipe is there some other library that I could use?? I heard about openPose too but when I tried it didn't work someone was saying it works only on windows 11. There are some other parts also to this project which will mostly be dealt with Pytorch. There are some pose estimators in TensorFlow but I want to stick to pytorch hence would like some pose estimators in that framwork, or a library or somehing inside opencv https://preview.redd.it/w9mioiyxhvob1.png?width=951&format=png&auto=webp&s=3886c356513b62efbcaddaa76841457cf3eb22e5 https://preview.redd.it/xjal9kyxhvob1.png?width=506&format=png&auto=webp&s=7f544a3050fbd744d300d2bf6e1a286a4014ece5 submitted by /u/rakk109 [link] [comments]  ( 9 min )

submitted by /u/Smith4242 [link] [comments]  ( 9 min )

Hello all, I've come across a fascinating example of virtual staging and I'm looking for some technical advice. Here's the image: https://preview.redd.it/3vbw441eeuob1.png?width=2511&format=png&auto=webp&s=679bc62f0cb61d479fe6dc6ce93af4f8846b8cea I get how ControlNet-MLSD is used to generate the lines and structure of the empty room. My question is, how is the furniture generated and added to the room without messing up the pixels, making it look as realistic as in the example? submitted by /u/dexter-dot [link] [comments]  ( 9 min )

Hi Everyone, Which vector database would be efficient and affordable for a enterprise chatbot? I tried Pinecone, its was simple to integrate with my python backend. But it's not open-source and its pricing it bit concerning. So Please suggest an alternative. submitted by /u/Free_Conversation106 [link] [comments]  ( 9 min )

Basically i wanted to understand how backprop is done in neural networks and how i should be implementing it, so i did what i always do - the math. I just want to know if what i though up is even usable in practice or not. Here is my math. submitted by /u/EnderPoint07 [link] [comments]  ( 9 min )

Hi, all. ​ I just read the paper named " SELF-ATTENTION DOES NOT NEED O(n 2 ) MEMORY" from Google. I understood that it requires O(1) for a single query, but still cannot understand why it requires O(log N) for self-attention and different order input. ​ It seems like adding one index into a sequence requires O(log N) (The paper's saying this). But why does it take O(log N)? Isn't it just O(1)? Because it is just adding a single datapoint for the index. ​ I really hope someone understands why it is and leaves any comment on this. Here's the paper. https://arxiv.org/abs/2112.05682 ​ Thanks in advance. submitted by /u/Maximum_Performance_ [link] [comments]  ( 9 min )

I need some help regarding the process of converting ONNX model to WASM format I created ELECTRA discriminator model with my own config, then convert the Pytorch model to ONNX format. After that, I quantized the model to 2mb. The model will be used for text classification. Now I want to convert it to WASM, but I'm literally stucked and dont know how to proceed I need some suggestions on how to proceed Please help, thank you submitted by /u/Ellzaf [link] [comments]  ( 9 min )

LIke the title says, i have correlated data samples and a covariance matrix among them. if i use a fcnn i can only consider the samples i.i.d. and the use either the MSE or THE MLE as loss function. but the data samples are not independent, so what architecture would allow me to use the full covariance matrix among the samples? transformers? submitted by /u/ilrazziatore [link] [comments]  ( 9 min )

I have written and submitted a paper to TMLR and also am uploading it to arxiv. However, even after using \usepackage[preprint]{tmlr}, I'm getting "Under Submission at TMLR". Should this happen. If not, where am I going wrong? submitted by /u/filletedforeskin [link] [comments]  ( 9 min )

i.e the goal of learning to model the empirical distribution is misaligned with the goal of modeling the "true" distribution. I've found this framing helpful for describing regulirization heuristics to people, is this a valid way of viewing it? submitted by /u/Cartesian_Carrot [link] [comments]  ( 9 min )

I have asked this question in other subreddits but no one answered me yet.I've googled it, but maybe some kind people who actually have worked or are working in this field would share some resources. Maybe there are some books or papers that are very explanatory and directly show what problems can be solved by using ML in prosthetics, how and etc. Maybe there are introductory textbooks or must-read papers. submitted by /u/tenderwrath [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07864 Github: https://github.com/WooooDyy/LLM-Agent-Paper-List Abstract: For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions. Many efforts have been made to develop intelligent AI agents since the mid-20th century. However, these efforts have mainly focused on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks. Actually, what the community lacks is a sufficiently general and powerful model to serve as a starting point for designing AI agents that can adapt to diverse …  ( 10 min )

Since the influx caused by LLMs, this sub has become almost useless to me. What are some alternatives where interesting papers are shared, research discussions take place, and which isn't flooded with LLMs, startups, or personal projects? submitted by /u/ParanoidTire [link] [comments]  ( 9 min )

Hey all Please don't mind my English writing I have a dataset with scaled feature (scaled by StanderScaler) and multiple class target variable encoded as 0,1,2..6 Should I scale the target variable like the feature to increase the accuracy (current accuracy is 79%) and if so how can I do this submitted by /u/Sunday_A [link] [comments]  ( 9 min )

Hello, ​ I am an information systems student currently conducting research for my undergraduate thesis on the factors that influence people's adoption intention of ChatGPT, as well as identifying the factors that may be holding them back. These factors include people's concerns about potential negative impacts of ChatGPT, such as increased unemployment and the spread of misinformation. Your participation in this study is crucial as it will provide valuable insights to help us understand how ChatGPT can be improved to meet users' needs. ​ Please note that I am not affiliated with OpenAI, no identifying information will be collected during the survey, and all responses will be kept confidential. The survey should take approximately 10 to 15 minutes to complete, and participation is voluntary. You may withdraw from the survey at any time, and there are no known risks associated with participating. ​ If you are interested in learning more about the study, please follow the link below. ​ https://docs.google.com/forms/d/e/1FAIpQLSf5HIfXHppMuTR63x00i4OuRAtM5Ti6EGybd-HuI1kmK06VPw/viewform?usp=sf_link ​ Thank you for taking the time to contribute to our research study. Your participation is greatly appreciated! submitted by /u/maulanashi [link] [comments]  ( 9 min )

When I ask questions related to security issues through the APIs of ChatGPT, Claude and other LLMs, such as inquiring how to make a bomb, the APIs of these LLMs would often refuse to answer. How do the APIs of these LLMs determine whether they should answer a question? Do they make judgments based on pre-generated responses? Or do they match keywords in the input prompt? Or do they use a classifier to identify the input prompt? submitted by /u/ShacklesLay [link] [comments]  ( 9 min )

People have been chasing super-smart AI for ages, hoping they could think and act like us. While we've made a lot of cool tech, we still need a killer starting point for AI that can handle all sorts of tasks. Large Language Models" (LLMs) are like a big leap toward AI that's smart across the board. People have been using LLMs to make AI that can do loads of things. The article takes us on a trip from where AI ideas started, to why LLMs rock as the backbone for AI. https://arxiv.org/abs/2309.07864 They break down this LLM-AI into three parts: the thinky bit (brain), what they sense (perception), and what they do (action). They chat about how these AI can work solo, in teams, or buddy up with humans. https://arxiv.org/abs/2309.07864 submitted by /u/QuantumAsha [link] [comments]  ( 9 min )

Beginning new career in a couple months, would like to upskill on AI utilization and usage cases. I won’t need to code in this role but rather understand how to use existing tools in an optimal way and recommend use cases to clients. What courses would be optimal to gain that skill set? submitted by /u/iceflamemaster [link] [comments]  ( 9 min )

AI, including ChatGPT, has narrow expertise and lacks the broad spectrum of human intelligence. The training of AI models can be costly due to hardware, data collection, and energy consumption. The trustworthiness of training data is crucial for reliable AI models, but issues like bias, labeling errors, and data privacy can affect performance. AI systems are vulnerable to adversarial attacks, such as manipulating input data to deceive the models. AI lacks genuine understanding, emotional/social intelligence, common sense/critical thinking, and true creativity. Source : https://blog.edned.net/will-ai-replace-cyber-security/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

what app that has custom charaters and voice chat? i forgor 💀 please 😭 it has image gen roleplay rooms etc submitted by /u/roblox22g [link] [comments]  ( 9 min )

Google is rolling out its third iteration of the Helpful Content Update, which aims to classify content as either 'written for search engines' or 'written for people'. The update reflects Google's realization that it can't accurately police AI-generated content and emphasizes the importance of creating content for people-first, regardless of the means used to create it. Detecting AI content is challenging, as AI detection tools often classify content based on tone, leading to false positives. Google's change in stance is not surprising, considering their heavy investment in AI, including chatbot Bard and new search features like the Search Generative Experience. The majority of brands now openly share articles and guides on how to use AI tools to enhance marketing strategies and create actionable content plans quickly. However, the quality and value of AI-generated content remain important factors for success, as poorly generated content can harm a brand's reputation and ranking. Source : https://stackdiary.com/google-changes-its-stance-on-ai-generated-content/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

The geometric description of addition of points P and Q on an elliptic curve involves four logical branches: If one of P or Q is the point at infinity … Else if P = Q … Else if P and Q lie on a vertical line … Else … It would seem that an algorithm […] Elliptic curve addition formulas first appeared on John D. Cook.  ( 6 min )

Mathematicians often speak informally about the relative simplicity of rational numbers. For example, musical intervals that correspond to simple fractions have less tension than intervals that correspond to more complicated fractions. Such informal statements can be made more precise using height functions. There are a variety of height functions designed for different applications, but the […] Rational height functions first appeared on John D. Cook.  ( 6 min )

A curated reading list for the adversarial perspective in deep reinforcement learning. https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning submitted by /u/ml_dnn [link] [comments]  ( 9 min )

submitted by /u/keghn [link] [comments]  ( 9 min )

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I am particularly interested in the ideas that can have high research potential and impact to the RL field. submitted by /u/C7501 [link] [comments]  ( 9 min )

An agent is tasked to learn to navigate and collect orbs: Solution space in blue View Poll submitted by /u/FriendlyStandard5985 [link] [comments]  ( 9 min )

Hi, I would like to try test the adversarial policy (https://arxiv.org/abs/1905.10615) in petting-zoo/melting-pot environment. I wonder if there are any built-in agents besides random? Do you know any repos with Sota agents in one of those environments? submitted by /u/MrCogito_hs [link] [comments]  ( 9 min )

Hi all, I am training a small model (120k params) on a custom grid-world environment I built with JAX. I was able to train the model very well with PPO on 1 GPU, but when I scaled to multiple GPUs (tried with 6 in parallel), the training curves showed a lot more variance than what I was seeing on 1 GPU. I did not change the hyperparams, I just spawned the same number of environments (~7000 per GPU) on multiple devices. The multi-GPU setup works in the following way: - I keep parallel independent buffers, one on each device - I initialize identical models on each device - I get independent gradients on each device at the update step, then I take the mean of the gradients across the devices and then I backpropagate the same gradients on each device independently. (I checked that after some time the models are still identical, and that is the case). Now the question is, what could be the reason for such an increase in variance? What can I try to mitigate the problem? Here's a comparison of the entropy curves... P.S. The model still trains quite well, but I guess that if I manage to make the curves smoother it is going to train much faster and to a better performance. https://preview.redd.it/4m01uirjfsob1.png?width=1826&format=png&auto=webp&s=1e1a79b9f4cdefe019bb16ccb7e11fd92dd261e3 submitted by /u/arbueticos [link] [comments]  ( 9 min )

submitted by /u/keghn [link] [comments]  ( 8 min )

Hi there, I've created a video here where I explain how the Grounding DINO model works for open-set object detection. I hope it may be of use to some of you out there. Feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 9 min )

https://news.accelerationrobotics.com/hardware-acceleration-in-robotics-77/ Hardware acceleration in robotics news. Modi wants to make India a chip-making superpower. Can he?, What's new in China's robotics market?, July chip sales edge up, but are still well behind last year, Rockwell automation acquiring AMR developer Clearpath robotics submitted by /u/pablocarrera [link] [comments]  ( 9 min )

I noticed that the default argument for `deterministic` in DQN is false. But how would that work? Typically DQN is trained with a deterministic function approximator. How would the algorithm become stochastic during inference time? In DQN the final layer activation is linear and therefore I don't see how one could even make this algorithm stochastic, unlike policy gradient where the final layer is softmax or Normal. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

I have read these papers "learning to reinforcement learn" and "PFC as meta RL system". The authors claim that when RNN is trained on multiple tasks from a task distribution using a model free RL algorithm, another model based RL algorithm emerges within the activation dynamics of RNN. The RNN with resulting activations acts as a standalone model based RL system on a new task(from the same task distribution) even after freezing the weights of outer loop model free algorithm of that. I couldn't understand how an RNN with only fixed activations act as RL? Can someone help? submitted by /u/C7501 [link] [comments]  ( 9 min )

I'm currently exploring the application of reinforcement learning to address a challenge within the power market. Specifically, I'm focused on devising an optimal strategy for electricity bidding, encompassing both buying and selling options, across different hours of the day. Imagine we have a power generator capable of producing up to 800 MW of electricity daily, with a charging rate of up to 200 MW per hour. After continuously charging it for four hours, it reaches its maximum capacity, and further charging is restricted until some electricity is discharged. Our dataset spans the past 3 years and contains vital information such as temperature, hydro availability, gas prices, and locational marginal prices, which are pivotal in determining profitability. For instance, if we decide to pu…  ( 10 min )

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submitted by /u/No-Percentage7346 [link] [comments]  ( 9 min )

I'm taking a No code ML class and we are asked to choose which platform we want to use. The options are Dataiku, RapidMiner, and KNIME. Does anyone have thoughts on these options in terms of which is best/worst for someone with minimal coding experience? submitted by /u/V1ncentAdultman [link] [comments]  ( 9 min )

Hi everybody, I have been working on scalable GNN training for a while and noticed that the bottleneck of training GNNs is the graph sampling and feature fetching stages. GNN training frameworks PyG and DGL that most people use seem to default to using Neighbor Sampling for minibatch training. I am hoping that with my new paper Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs, this default might be due to change. My new approach combines the layer sampling approach with the neighbor sampling approach. The result is that there is more overlap in the sampled neighborhoods and one still gets a fixed number of neighbors per seed vertex. It is even possible to turn it into a subgraph sampling approach by fixing the random seed used for sampling across all layers of the GNN m…  ( 10 min )

Hi guys need some advice, I have been working as a data scientist for the past 3 years, mostly in the domain of time series & predictive analytics (churn prediction/segmentation, etc.) with some deployment, hence do not currently have any major NLP/CV/Deep learning projects as such. Now, I can see that NLP/CV/Gen AI is mostly in demand and they are really enjoyable as well. How do I shift into these domains, given that new companies having these roles are asking for similar past working experience? submitted by /u/immortal_omen [link] [comments]  ( 9 min )

submitted by /u/Mediocre-Bullfrog686 [link] [comments]  ( 9 min )

I am beginner in machine learning field. I know python, some basic machine learnig algorithm like linear, logistic, decision tree, random forest. I did some work on jupyter notebok related to machine learning like data gathering, data preprocessing, data modeling, data training, finding accuracy, confusion matrix, precision, recall, feature generation etc. i deployed one machine learning model on Skitlearn as well. Now, I just want to know that what next means what i can do more in machine learning field after this. I just need some guidance to move on further. I use kaggle and tensor flow (don't know exactly more about this ). If you have any suggestion or any guidance that will be appreciated. submitted by /u/myteachexplore [link] [comments]  ( 9 min )

Today we live in a world of multi-model LLMs. How will the following technologies fare against these LLM-based models? Neural VQA Semantic Scene Segmentation Multi-model LLM are emerging quickly now, (such as NExT-GPT https://next-gpt.github.io/ ) . When you consider the kind of "understanding" of a visual scene these models are capable of, what will happen to prior approaches like Neural VQA? The nagging feeling that Neural VQA is going to be completely superseded by LLMs is palpable. The only vestige left for the older technology may have something to do with reasoning about the objects , such as properly counting the number of objects of a category that are present. But even that is getting sketchy. On the topic of scene understanding, we can turn to semantic scene segmentation. SSS is a more complicated topic than Neural VQA. SOTA SSS algorithms are still largely employing DeConv Nets, and still require fully labelled datasets. With multi-model LLMs, there is a nagging question : Why go through the complexity/mess of first segmenting a scene very accurately, when an LLM can do better at identifying the entire scene's category in one fail swoop? One might suggest that SSS still has a use in regards to interacting with the segmented objects of an environment, where one such "interaction" would be avoiding collisions with pedestrians, trees, or other cars. But honestly, SSS does not really make this connection with planning and action, it really only gives you the categories of the segments. THe autonomous vehicle's next moves are still an open problem. What technologies do you expect that multi-model LLMs will supersede, if any? submitted by /u/moschles [link] [comments]  ( 9 min )

submitted by /u/reps_up [link] [comments]  ( 9 min )

Hey. First of all I'm not a researcher on this area, so pardon my ignorance. I'm looking to employ a GAN on a dataset. The goals are still a bit unclear, but it's mainly to improve classification by either data augmentation and class balancing, or understanding the data through the latent representations. I'm really keen on InfoGAN at the moment. I trained one on the MNIST and the continuous variables learned the rotation and width, as in the paper and other peoples code. But at this point I think I need some help. I have labels, which means that maybe I should use a conditional GAN. But how will I learn similar representations as those in InfoGAN? I know StyleGAN is the current big thing in this area, but my images are limited to similar pixel-range as MNIST, and StyleGAN seems dependant on the ProGAN idea of increasing resolution for training. I'm a bit confused on the whole topic and would love a pointer to any discussion etc., as I can't seem to find anything but papers and they seem to be only focused on human faces, my data is unnatural not unsimilar again to MNIST. I don't have semantical information either as I see many papers employing that. I see many papers employing semi-supervision in this area, but honestly I'm just a bit lost and overwhelmed as this is not my area and GAN papers are still not stopping (I read a post here from 2017 about a GAN making GANs...). If you read this far: thank you and any pointers and discussion are very welcome. I would post on /r/learnmachinelearning but I feel the discussion in there is very different from this. My main goal is data exploration, but also to prove effectiveness some classification will be necessary and here the generative approach may help to augment data efficiently. submitted by /u/Infamous-Bank-7739 [link] [comments]  ( 10 min )

Hi guys need some advice, I have been working as a data scientist for the past 3 years, mostly in the domain of time series & predictive analytics, hence do not currently have any major NLP/CV/Deep learning projects as such. Now, I can see that NLP/CV/Gen AI is mostly in demand and they are really enjoyable as well. How do I shift into these domains, given that new companies having these roles are asking for similar past working experience? ​ submitted by /u/immortal_omen [link] [comments]  ( 9 min )

I have been working on a alternative to Jupyter Notebooks. Please check it out and share your thoughts : https://github.com/nottherealsanta/ganimede ​ https://preview.redd.it/k8rcx8fwrjob1.png?width=2302&format=png&auto=webp&s=a8a670251f6c268acffc88a40bd528d8d438a5f5 submitted by /u/notsorealsanta [link] [comments]  ( 9 min )

How would you evaluate generated spectrogram audio quality? Taking Riffusion for example, how would you then compare its performance to another generator? What are some common techniques that I could use? I mean of course purely in the quality of the audio itself, not my subjective opinion on how much I like the music submitted by /u/DavesEmployee [link] [comments]  ( 9 min )

In part 1 of this series on the updated “AI Apps Development Canvas,” I introduced the updated AI Apps Product Development Design Canvas.  The AI Apps Product Development Canva is one of the capstone deliverables for my “Thinking Like a Data Scientist” methodology, so getting feedback is critical to ensure that the methodology is relevant… Read More »AI Apps Product Development Canvas – Part 2 The post AI Apps Product Development Canvas – Part 2 appeared first on Data Science Central.  ( 25 min )

The COVID-19 pandemic and the January 6th insurrection have had a profound impact on people's feelings. Many people have experienced a range of negative emotions, including: Fear and anxiety: The pandemic has brought with it a constant fear of getting sick or losing loved ones. The insurrection also raised fears about the future of democracy and the safety of the country. Grief and loss: Millions of people have lost loved ones to COVID-19. Many others have experienced loss in other ways, such as losing their jobs, businesses, or homes. The insurrection also led to loss of trust in the government and in each other. Anger and frustration: Many people are angry about the way the pandemic and the insurrection have been handled. They may be frustrated with government officials, with their …  ( 10 min )

There is no one definitive answer to this question, as the percentage of our thoughts that come to us on their own without our consciously choosing them can vary depending on the individual and the situation. However, some estimates suggest that up to 95% of our thoughts may be involuntary. This is because our thoughts are constantly being influenced by a variety of factors, both external and internal. For example, our environment, our emotions, and our physical state can all play a role in determining what thoughts pop into our heads. In addition, our brains are constantly processing information and making predictions, even when we are not actively thinking about anything. This process can sometimes lead to spontaneous thoughts, such as when we suddenly remember something that we had forgotten or when we come up with a new idea. While some of our spontaneous thoughts may be negative or intrusive, many of them can also be helpful or creative. For example, a spontaneous thought may help us to solve a problem, come up with a new solution, or simply make us smile. Overall, it is clear that a significant portion of our thoughts come to us without our consciously choosing them. This is a natural part of the human cognitive process and can be both beneficial and detrimental. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

I have a lighting business and I have LOADS of videos, time lapses, images from our projects. But I can't make decent videos with them for social media. I'd love to find an ai service where I can upload a projects media and prompt the ai with exactly what I want. Like, please use this content to create marketing videos for us on tiktok, Instagram and facebook Facebook Etc submitted by /u/RulerOfThePixel [link] [comments]  ( 9 min )

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A little boy named Alex saw 17 different doctors over the course of three years, unable to find a root cause of his chronic pain. At her wit’s end, his mom, Courtney, fed his radiology report into ChatGPT and produced immediate answers.[1] In January, Wharton professor Christian Terwiesch gave his MBA final exam to ChatGPT. It passed with flying colors. Now, he’s at it again with a new experiment to determine whether ChatGPT can come up with product ideas better and faster than his students. It can. And cheaper, too.[2] Bathroom-cleaning robot built for commercial businesses gives consumers hope for AI maid.[3] Judge admits he used ChatGPT to write a Court of Appeal ruling as he calls the AI tool ‘jolly useful’.[4] Sources: [1] https://radiologybusiness.com/topics/artificial-intelligence/after-seeing-17-different-doctors-boy-rare-condition-receives-diagnosis-chatgpt [2] https://knowledge.wharton.upenn.edu/article/is-chatgpt-a-better-entrepreneur-than-most/ [3] https://www.foxnews.com/lifestyle/bathroom-cleaning-robot-built-commercial-businesses-gives-consumers-hope-ai-maid [4] https://www.dailymail.co.uk/news/article-12524607/Judge-ChatGPT-write-Court-Appeal-ruling-AI-useful.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning. On the other hand, these steps are becoming ever increasingly delicate due to the extensive rise in the number, diversity, and distributedness of machine learning resources. Multi-agent systems, when applied to the design of machine learning platforms, bring about several distinctive characteristics such as scalability, flexibility, and robustness, just to name a few. This paper proposes a fully automatic and collaborative agent-based mechanism for selecting distributedly organized machine learning algorithms and simultaneously tuning their hyperparameters. Our method builds upon an existing agent-based hierarchical machine-learning platform and augments its query structure to support the aforementioned functionalities without being limited to specific learning, selection, and tuning mechanisms. We have conducted theoretical assessments, formal verification, and analytical study to demonstrate the correctness, resource utilization, and computational efficiency of our technique. According to the results, our solution is totally correct and exhibits linear time and space complexity in relation to the size of available resources. To provide concrete examples of how the proposed methodologies can effectively adapt and perform across a range of algorithmic options and datasets, we have also conducted a series of experiments using a system comprised of 24 algorithms and 9 datasets.  ( 3 min )

We define a family of $C^1$ functions which we call "nowhere coexpanding functions" that is closed under composition and includes all $C^3$ functions with non-positive Schwarzian derivative. We establish results on the number and nature of the fixed points of these functions, including a generalisation of a classic result of Singer.  ( 2 min )

Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Recently, various studies have achieved promising results. However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations. In real-life scenarios, the deployment of sensors could be limited due to budget limitations and installation availability, which makes most current models not applicable. Though few pieces of literature tried to impute traffic states at the missing locations, these methods need the data simultaneously observed at the locations with sensors, making them not applicable to prediction tasks. Another drawback is the lack of measurement of uncertainty in prediction, making prior works unsuitable for risk-sensitive tasks or involving decision-making. To fill the gap, inspired by the previous inductive graph neural network, this work proposed an uncertainty-aware framework with the ability to 1) extend prediction to missing locations with no historical records and significantly extend spatial coverage of prediction locations while reducing deployment of sensors and 2) generate probabilistic prediction with uncertainty quantification to help the management of risk and decision making in the down-stream tasks. Through extensive experiments on real-life datasets, the result shows our method achieved promising results on prediction tasks, and the uncertainty quantification gives consistent results which highly correlated with the locations with and without historical data. We also show that our model could help support sensor deployment tasks in the transportation field to achieve higher accuracy with a limited sensor deployment budget.  ( 3 min )

As recently demonstrated, Deep Neural Networks (DNN), usually trained using single precision IEEE 754 floating point numbers (binary32), can also work using lower precision. Therefore, 16-bit and 8-bit compressed format have attracted considerable attention. In this paper, we focused on two families of formats that have already achieved interesting results in compressing binary32 numbers in machine learning applications, without sensible degradation of the accuracy: bfloat and posit. Even if 16-bit and 8-bit bfloat/posit are routinely used for reducing the storage of the weights/biases of trained DNNs, the inference still often happens on the 32-bit FPU of the CPU (especially if GPUs are not available). In this paper we propose a way to decompress a tensor of bfloat/posits just before computations, i.e., after the compressed operands have been loaded within the vector registers of a vector capable CPU, in order to save bandwidth usage and increase cache efficiency. Finally, we show the architectural parameters and considerations under which this solution is advantageous with respect to the uncompressed one.  ( 2 min )

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection. However, the size of LLMs (i.e., billions of parameters) requires highly effective compression to fit into storage-limited devices. Among many compression techniques, weight-clustering, a form of non-linear quantization, is one of the leading candidates for LLM compression, and supported by modern smartphones. Yet, its training overhead is prohibitively significant for LLM fine-tuning. Especially, Differentiable KMeans Clustering, or DKM, has shown the state-of-the-art trade-off between compression ratio and accuracy regression, but its large memory complexity makes it nearly impossible to apply to train-time LLM compression. In this paper, we propose a memory-efficient DKM implementation, eDKM powered by novel techniques to reduce the memory footprint of DKM by orders of magnitudes. For a given tensor to be saved on CPU for the backward pass of DKM, we compressed the tensor by applying uniquification and sharding after checking if there is no duplicated tensor previously copied to CPU. Our experimental results demonstrate that \prjname can fine-tune and compress a pretrained LLaMA 7B model from 12.6 GB to 2.5 GB (3bit/weight) with the Alpaca dataset by reducing the train-time memory footprint of a decoder layer by 130$\times$, while delivering good accuracy on broader LLM benchmarks (i.e., 77.7% for PIQA, 66.1% for Winograde, and so on).  ( 3 min )

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to fluid mechanical principles, we rationalise the selection of novel design features that lead to experimental performance improvements of ~60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.  ( 2 min )

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than $0.21$ kcal/mol for most of the docking targets, showing significant improvement over conventional circular fingerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the transferable model exhibits superb training and data efficiency. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.  ( 3 min )

Traditional adversarial attacks concentrate on manipulating clean examples in the pixel space by adding adversarial perturbations. By contrast, semantic adversarial attacks focus on changing semantic attributes of clean examples, such as color, context, and features, which are more feasible in the real world. In this paper, we propose a framework to quickly generate a semantic adversarial attack by leveraging recent diffusion models since semantic information is included in the latent space of well-trained diffusion models. Then there are two variants of this framework: 1) the Semantic Transformation (ST) approach fine-tunes the latent space of the generated image and/or the diffusion model itself; 2) the Latent Masking (LM) approach masks the latent space with another target image and local backpropagation-based interpretation methods. Additionally, the ST approach can be applied in either white-box or black-box settings. Extensive experiments are conducted on CelebA-HQ and AFHQ datasets, and our framework demonstrates great fidelity, generalizability, and transferability compared to other baselines. Our approaches achieve approximately 100% attack success rate in multiple settings with the best FID as 36.61. Code is available at https://github.com/steven202/semantic_adv_via_dm.  ( 2 min )

Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. In this study, we propose a meta-learning algorithm called ConDex, which incorporates Conditional Neural Processes (CNP) with DexNet-2.0 to autonomously discern the underlying physical properties of objects using depth images. ConDex efficiently acquires physical embeddings from limited trials, enabling precise grasping point estimation. Furthermore, ConDex is capable of updating the predicted grasping quality iteratively from new trials in an online fashion. To the best of our knowledge, we are the first who generate two object datasets focusing on inhomogeneous physical properties with varying mass distributions and friction coefficients. Extensive evaluations in simulation demonstrate ConDex's superior performance over DexNet-2.0 and existing meta-learning-based grasping pipelines. Furthermore, ConDex shows robust generalization to previously unseen real-world objects despite training solely in the simulation. The synthetic and real-world datasets will be published as well.  ( 2 min )

Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes attached to different parts of a patient to analyse the electorencephal data rich with spatial and temporal features for health assessment and disease diagnosis. Existing research has mainly used deep learning techniques such as convolutional neural network (CNN) or recurrent neural network (RNN) to extract hidden spatial-temporal features. Yet, it is challenging to incorporate both inter-dependencies spatial information and dynamic temporal changes simultaneously. In reality, for a model that leverages these spatial-temporal features to fulfil complex prediction tasks, it often requires a colossal amount of training data in order to obtain satisfactory model performance. Considering the above-mentioned challenges, we propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning in this paper. After transforming the raw spatial-temporal data into high quality features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs capable of capturing the hidden topological and long-term temporal correlation information in these graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module in our framework to leverage diverse data distributions from different participants with attentive aggregations of their models.

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git.  ( 2 min )

More research attention has recently been given to end-to-end autonomous driving technologies where the entire driving pipeline is replaced with a single neural network because of its simpler structure and faster inference time. Despite this appealing approach largely reducing the components in driving pipeline, its simplicity also leads to interpretability problems and safety issues arXiv:2003.06404. The trained policy is not always compliant with the traffic rules and it is also hard to discover the reason for the misbehavior because of the lack of intermediate outputs. Meanwhile, Sensors are also critical to autonomous driving's security and feasibility to perceive the surrounding environment under complex driving scenarios. In this paper, we proposed P-CSG, a novel penalty-based imitation learning approach with cross semantics generation sensor fusion technologies to increase the overall performance of End-to-End Autonomous Driving. We conducted an assessment of our model's performance using the Town 05 Long benchmark, achieving an impressive driving score improvement of over 15%. Furthermore, we conducted robustness evaluations against adversarial attacks like FGSM and Dot attacks, revealing a substantial increase in robustness compared to baseline models.More detailed information, such as code-based resources, ablation studies and videos can be found at https://hk-zh.github.io/p-csg-plus.  ( 2 min )

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.  ( 2 min )

Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inductive conformal prediction (ICP). This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data. The efficacy of the method is validated across three classification tasks within distinct modalities: filtering drug-induced-liver-injury (DILI) literature with title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced through label permutation. Results show significant enhancements in classification performance: accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% and 89.0%). Our method offers the potential to substantially boost classification performance in multi-modal biomedical machine learning tasks. Importantly, it accomplishes this without necessitating an excessive volume of meticulously curated training data.  ( 3 min )

Effectively leveraging multimodal information from social media posts is essential to various downstream tasks such as sentiment analysis, sarcasm detection and hate speech classification. However, combining text and image information is challenging because of the idiosyncratic cross-modal semantics with hidden or complementary information present in matching image-text pairs. In this work, we aim to directly model this by proposing the use of two auxiliary losses jointly with the main task when fine-tuning any pre-trained multimodal model. Image-Text Contrastive (ITC) brings image-text representations of a post closer together and separates them from different posts, capturing underlying dependencies. Image-Text Matching (ITM) facilitates the understanding of semantic correspondence between images and text by penalizing unrelated pairs. We combine these objectives with five multimodal models, demonstrating consistent improvements across four popular social media datasets. Furthermore, through detailed analysis, we shed light on the specific scenarios and cases where each auxiliary task proves to be most effective.  ( 2 min )

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power.  ( 3 min )

Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD ) prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.  ( 3 min )

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at https://github.com/zjunlp/Prompt4ReasoningPapers (updated periodically).  ( 2 min )

Credibility signals represent a wide range of heuristics that are typically used by journalists and fact-checkers to assess the veracity of online content. Automating the task of credibility signal extraction, however, is very challenging as it requires high-accuracy signal-specific extractors to be trained, while there are currently no sufficiently large datasets annotated with all credibility signals. This paper investigates whether large language models (LLMs) can be prompted effectively with a set of 18 credibility signals to produce weak labels for each signal. We then aggregate these potentially noisy labels using weak supervision in order to predict content veracity. We demonstrate that our approach, which combines zero-shot LLM credibility signal labeling and weak supervision, outperforms state-of-the-art classifiers on two misinformation datasets without using any ground-truth labels for training. We also analyse the contribution of the individual credibility signals towards predicting content veracity, which provides new valuable insights into their role in misinformation detection.  ( 2 min )

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yields the highest average performance across all criteria.  ( 2 min )

Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation between feature channels is usually expected to be learned from the training data, requiring numerous parameters and careful training. In contrast, vector-valued neural networks are conceived to process arrays of vectors and naturally consider the intercorrelation between feature channels. Consequently, they usually have fewer parameters and often undergo more robust training than traditional neural networks. This paper aims to present a broad framework for vector-valued neural networks, referred to as V-nets. In this context, hypercomplex-valued neural networks are regarded as vector-valued models with additional algebraic properties. Furthermore, this paper explains the relationship between vector-valued and traditional neural networks. Precisely, a vector-valued neural network can be obtained by placing restrictions on a real-valued model to consider the intercorrelation between feature channels. Finally, we show how V-nets, including hypercomplex-valued neural networks, can be implemented in current deep-learning libraries as real-valued networks.  ( 2 min )

The study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG). We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We also investigated different combinations of signal frequency ranges and EEG channels to accurately diagnose PD. Finally, with a fewer number of features (11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%. The most prominent frequency range of EEG signals has been identified, and we have found that high classification accuracy depends on low-frequency signal components (0-4 Hz). Moreover, the most informative signals were mainly received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore, we assessed the accuracy of the diagnosis of PD using three different lengths of EEG data (150-1000 samples). Because the computational complexity is reduced by reducing the input data. As a result, we have achieved a maximum mean accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By reducing the number of features and segment lengths, the computational cost of classification can be reduced. Lower-performance smart ML sensors can be used in IoT environments for enhances human resilience to PD.  ( 3 min )

In this work, we provide a characterization of the feature-learning process in two-layer ReLU networks trained by gradient descent on the logistic loss following random initialization. We consider data with binary labels that are generated by an XOR-like function of the input features. We permit a constant fraction of the training labels to be corrupted by an adversary. We show that, although linear classifiers are no better than random guessing for the distribution we consider, two-layer ReLU networks trained by gradient descent achieve generalization error close to the label noise rate. We develop a novel proof technique that shows that at initialization, the vast majority of neurons function as random features that are only weakly correlated with useful features, and the gradient descent dynamics 'amplify' these weak, random features to strong, useful features.  ( 2 min )

The primary goal of this research is to propose a novel architecture for a deep neural network that can solve fractional differential equations accurately. A Gaussian integration rule and a $L_1$ discretization technique are used in the proposed design. In each equation, a deep neural network is used to approximate the unknown function. Three forms of fractional differential equations have been examined to highlight the method's versatility: a fractional ordinary differential equation, a fractional order integrodifferential equation, and a fractional order partial differential equation. The results show that the proposed architecture solves different forms of fractional differential equations with excellent precision.  ( 2 min )

Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on classifying brain images into one of three major categories: healthy, MCI, and AD; or categorizing MCI patients into (1) progressive: those who progress from MCI to AD at a future examination time, and (2) stable: those who stay as MCI and never progress to AD. This misses the opportunity to accurately identify the trajectory of progressive MCI patients. In this paper, we revisit the brain image classification task for AD identification and re-frame it as an ordinal classification task to predict how close a patient is to the severe AD stage. To this end, we select progressive MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an ordinal dataset with a prediction target that indicates the time to progression to AD. We train a Siamese network model to predict the time to onset of AD based on MRI brain images. We also propose a Weighted variety of Siamese network and compare its performance to a baseline model. Our evaluations show that incorporating a weighting factor to Siamese networks brings considerable performance gain at predicting how close input brain MRI images are to progressing to AD. Moreover, we complement our results with an interpretation of the learned embedding space of the Siamese networks using a model explainability technique.  ( 3 min )

Reinforcement Learning views the maximization of rewards and avoidance of punishments as central to explaining goal-directed behavior. However, over a life, organisms will need to learn about many different aspects of the world's structure: the states of the world and state-vector transition dynamics. The number of combinations of states grows exponentially as an agent incorporates new knowledge, and there is no obvious weighted combination of pre-existing rewards or costs defined for a given combination of states, as such a weighting would need to encode information about good and bad combinations prior to an agent's experience in the world. Therefore, we must develop more naturalistic accounts of behavior and motivation in large state-spaces. We show that it is possible to use only the intrinsic motivation metric of empowerment, which measures the agent's capacity to realize many possible futures under a transition operator. We propose to scale empowerment to hierarchical state-spaces by using Operator Bellman Equations. These equations produce state-time feasibility functions, which are compositional hierarchical state-time transition operators that map an initial state and time when an agent begins a policy to the final states and times of completing a goal. Because these functions are hierarchical operators we can define hierarchical empowerment measures on them. An agent can then optimize plans to distant states and times to maximize its hierarchical empowerment-gain, allowing it to discover goals that bring about a more favorable coupling of its internal structure (physiological states) to its external environment (world structure & spatial state). Life-long agents could therefore be primarily animated by principles of compositionality and empowerment, exhibiting self-concern for the growth & maintenance of their own structural integrity without recourse to reward-maximization.  ( 3 min )

Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformers for ECG data is not yet realized, despite their widespread success in computer vision and natural language processing. In this work, we present a useful masked Transformer method for ECG classification referred to as MTECG, which expands the application of masked autoencoders to ECG time series. We construct a dataset comprising 220,251 ECG recordings with a broad range of diagnoses annoated by medical experts to explore the properties of MTECG. Under the proposed training strategies, a lightweight model with 5.7M parameters performs stably well on a broad range of masking ratios (5%-75%). The ablation studies highlight the importance of fluctuated reconstruction targets, training schedule length, layer-wise LR decay and DropPath rate. The experiments on both private and public ECG datasets demonstrate that MTECG-T significantly outperforms the recent state-of-the-art algorithms in ECG classification.  ( 2 min )

In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean geometries. In this paper, we present such a mechanism, designed to classify sequences of Symmetric Positive Definite matrices while preserving their Riemannian geometry throughout the analysis. We apply our method to automatic sleep staging on timeseries of EEG-derived covariance matrices from a standard dataset, obtaining high levels of stage-wise performance.

We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps. One step further from the existing studies using deep neural networks mainly focusing on global feature extraction, the proposed framework aims to combine local and global features using image processing techniques and the YOLO object detection to localize pressure profiles from different body parts and consider physical constraints. The proposed local feature extraction method generates two sets of high-level local features consisting of cropped pressure mapping and numerical features such as angular orientation, location on the mat, and pressure area. In addition, we adopt a knowledge distillation for regularization to preserve the knowledge of the global feature extraction and improve the performance of the exercise recognition. Our experimental results demonstrate a notable 11 percent improvement in F1 score for exercise recognition while preserving label-specific features.  ( 2 min )

In Changjun Fan et al. [Nature Communications https://doi.org/10.1038/s41467-023-36363-w (2023)], the authors present a deep reinforced learning approach to augment combinatorial optimization heuristics. In particular, they present results for several spin glass ground state problems, for which instances on non-planar networks are generally NP-hard, in comparison with several Monte Carlo based methods, such as simulated annealing (SA) or parallel tempering (PT). Indeed, those results demonstrate that the reinforced learning improves the results over those obtained with SA or PT, or at least allows for reduced runtimes for the heuristics before results of comparable quality have been obtained relative to those other methods. To facilitate the conclusion that their method is ''superior'', the authors pursue two basic strategies: (1) A commercial GUROBI solver is called on to procure a sample of exact ground states as a testbed to compare with, and (2) a head-to-head comparison between the heuristics is given for a sample of larger instances where exact ground states are hard to ascertain. Here, we put these studies into a larger context, showing that the claimed superiority is at best marginal for smaller samples and becomes essentially irrelevant with respect to any sensible approximation of true ground states in the larger samples. For example, this method becomes irrelevant as a means to determine stiffness exponents $\theta$ in $d>2$, as mentioned by the authors, where the problem is not only NP-hard but requires the subtraction of two almost equal ground-state energies and systemic errors in each of $\approx 1\%$ found here are unacceptable. This larger picture on the method arises from a straightforward finite-size corrections study over the spin glass ensembles the authors employ, using data that has been available for decades.

The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.

Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack, BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.

Score-based methods for learning Bayesain networks(BN) aim to maximizing the global score functions. However, if local variables have direct and indirect dependence simultaneously, the global optimization on score functions misses edges between variables with indirect dependent relationship, of which scores are smaller than those with direct dependent relationship. In this paper, we present an identifiability condition based on a determined subset of parents to identify the underlying DAG. By the identifiability condition, we develop a two-phase algorithm namely optimal-tuning (OT) algorithm to locally amend the global optimization. In the optimal phase, an optimization problem based on first-order Hilbert-Schmidt independence criterion (HSIC) gives an estimated skeleton as the initial determined parents subset. In the tuning phase, the skeleton is locally tuned by deletion, addition and DAG-formalization strategies using the theoretically proved incremental properties of high-order HSIC. Numerical experiments for different synthetic datasets and real-world datasets show that the OT algorithm outperforms existing methods. Especially in Sigmoid Mix model with the size of the graph being ${\rm\bf d=40}$, the structure intervention distance (SID) of the OT algorithm is 329.7 smaller than the one obtained by CAM, which indicates that the graph estimated by the OT algorithm misses fewer edges compared with CAM.Source code of the OT algorithm is available at https://github.com/YafeiannWang/optimal-tune-algorithm.

Predicting the dynamics of chaotic systems is one of the most challenging tasks for neural networks, and machine learning in general. Here we aim to predict the spatiotemporal chaotic dynamics of a high-dimensional non-linear system. In our attempt we use the TensorFlow library, representing the state of the art for deep neural networks training and prediction. While our results are encouraging, and show that the dynamics of the considered system can be predicted for short time, we also indirectly discovered an unexpected and undesirable behavior of the TensorFlow library. More specifically, the longer term prediction of the system's chaotic behavior quickly deteriorates and blows up due to the nondeterministic behavior of the TensorFlow library. Here we provide numerical evidence of the short time prediction ability, and of the longer term predictability blow up.

We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF). It is consists of a nonparametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash. DNCF has no parameterization dependent on training data, therefore has a strong generalization and robustness to adversarial image manipulation. During inference, we also encourage the network parameters to be nonnegative and create a bi-convex function on the input and parameters, and this adapts to second-order optimization algorithms with insufficient running time, having 10X acceleration over Deep Image Prior. With these tools, we empirically verify its capability to defend image classification deep networks against adversary attack algorithms in real-time.

Apache TVM (Tensor Virtual Machine), an open source machine learning compiler framework designed to optimize computations across various hardware platforms, provides an opportunity to improve the performance of dense matrix factorizations such as LU (Lower Upper) decomposition and Cholesky decomposition on GPUs and AI (Artificial Intelligence) accelerators. In this paper, we propose a new TVM autotuning framework using Bayesian Optimization and use the TVM tensor expression language to implement linear algebra kernels such as LU, Cholesky, and 3mm. We use these scientific computation kernels to evaluate the effectiveness of our methods on a GPU cluster, called Swing, at Argonne National Laboratory. We compare the proposed autotuning framework with the TVM autotuning framework AutoTVM with four tuners and find that our framework outperforms AutoTVM in most cases.

Focus in Explainable AI is shifting from explanations defined in terms of low-level elements, such as input features, to explanations encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. An agreed-upon notion of concept interpretability is missing, with the result that concepts used by both post-hoc explainers and concept-based neural networks are acquired through a variety of mutually incompatible strategies. Critically, most of these neglect the human side of the problem: a representation is understandable only insofar as it can be understood by the human at the receiving end. The key challenge in Human-interpretable Representation Learning (HRL) is how to model and operationalize this human element. In this work, we propose a mathematical framework for acquiring interpretable representations suitable for both post-hoc explainers and concept-based neural networks. Our formalization of HRL builds on recent advances in causal representation learning and explicitly models a human stakeholder as an external observer. This allows us to derive a principled notion of alignment between the machine representation and the vocabulary of concepts understood by the human. In doing so, we link alignment and interpretability through a simple and intuitive name transfer game, and clarify the relationship between alignment and a well-known property of representations, namely disentanglment. We also show that alignment is linked to the issue of undesirable correlations among concepts, also known as concept leakage, and to content-style separation, all through a general information-theoretic reformulation of these properties. Our conceptualization aims to bridge the gap between the human and algorithmic sides of interpretability and establish a stepping stone for new research on human-interpretable representations.

We introduce a machine-learning framework to warm-start fixed-point optimization algorithms. Our architecture consists of a neural network mapping problem parameters to warm starts, followed by a predefined number of fixed-point iterations. We propose two loss functions designed to either minimize the fixed-point residual or the distance to a ground truth solution. In this way, the neural network predicts warm starts with the end-to-end goal of minimizing the downstream loss. An important feature of our architecture is its flexibility, in that it can predict a warm start for fixed-point algorithms run for any number of steps, without being limited to the number of steps it has been trained on. We provide PAC-Bayes generalization bounds on unseen data for common classes of fixed-point operators: contractive, linearly convergent, and averaged. Applying this framework to well-known applications in control, statistics, and signal processing, we observe a significant reduction in the number of iterations and solution time required to solve these problems, through learned warm starts.

We consider nonconvex-concave minimax problems, $\min_{\mathbf{x}} \max_{\mathbf{y} \in \mathcal{Y}} f(\mathbf{x}, \mathbf{y})$, where $f$ is nonconvex in $\mathbf{x}$ but concave in $\mathbf{y}$ and $\mathcal{Y}$ is a convex and bounded set. One of the most popular algorithms for solving this problem is the celebrated gradient descent ascent (GDA) algorithm, which has been widely used in machine learning, control theory and economics. Despite the extensive convergence results for the convex-concave setting, GDA with equal stepsize can converge to limit cycles or even diverge in a general setting. In this paper, we present the complexity results on two-time-scale GDA for solving nonconvex-concave minimax problems, showing that the algorithm can find a stationary point of the function $\Phi(\cdot) := \max_{\mathbf{y} \in \mathcal{Y}} f(\cdot, \mathbf{y})$ efficiently. To the best our knowledge, this is the first nonasymptotic analysis for two-time-scale GDA in this setting, shedding light on its superior practical performance in training generative adversarial networks (GANs) and other real applications.

Artificial intelligence models and methods commonly lack causal interpretability. Despite the advancements in interpretable machine learning (IML) methods, they frequently assign importance to features which lack causal influence on the outcome variable. Selecting causally relevant features among those identified as relevant by these methods, or even before model training, would offer a solution. Feature selection methods utilizing information theoretical quantities have been successful in identifying statistically relevant features. However, the information theoretical quantities they are based on do not incorporate causality, rendering them unsuitable for such scenarios. To address this challenge, this article proposes information theoretical quantities that incorporate the causal structure of the system, which can be used to evaluate causal importance of features for some given outcome variable. Specifically, we introduce causal versions of entropy and mutual information, termed causal entropy and causal information gain, which are designed to assess how much control a feature provides over the outcome variable. These newly defined quantities capture changes in the entropy of a variable resulting from interventions on other variables. Fundamental results connecting these quantities to the existence of causal effects are derived. The use of causal information gain in feature selection is demonstrated, highlighting its superiority over standard mutual information in revealing which features provide control over a chosen outcome variable. Our investigation paves the way for the development of methods with improved interpretability in domains involving causation.

Current physics-informed (standard or operator) neural networks still rely on accurately learning the initial conditions of the system they are solving. In contrast, standard numerical methods evolve such initial conditions without needing to learn these. In this study, we propose to improve current physics-informed deep learning strategies such that initial conditions do not need to be learned and are represented exactly in the predicted solution. Moreover, this method guarantees that when a DeepONet is applied multiple times to time step a solution, the resulting function is continuous.

Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage. We illustrate the new methods with examples from the U(n) Lie group family, obtaining the respective subalgebra decompositions. As an application to particle physics, we demonstrate the identification of the residual symmetries after the spontaneous breaking of non-Abelian gauge symmetries like SU(3) and SU(5) which are commonly used in model building.

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNet

Adversarial robustness, which primarily comprises sensitivity-based robustness and spatial robustness, plays an integral part in achieving robust generalization. In this paper, we endeavor to design strategies to achieve universal adversarial robustness. To achieve this, we first investigate the relatively less-explored realm of spatial robustness. Then, we integrate the existing spatial robustness methods by incorporating both local and global spatial vulnerability into a unified spatial attack and adversarial training approach. Furthermore, we present a comprehensive relationship between natural accuracy, sensitivity-based robustness, and spatial robustness, supported by strong evidence from the perspective of robust representation. Crucially, to reconcile the interplay between the mutual impacts of various robustness components into one unified framework, we incorporate the \textit{Pareto criterion} into the adversarial robustness analysis, yielding a novel strategy called Pareto Adversarial Training for achieving universal robustness. The resulting Pareto front, which delineates the set of optimal solutions, provides an optimal balance between natural accuracy and various adversarial robustness. This sheds light on solutions for achieving universal robustness in the future. To the best of our knowledge, we are the first to consider universal adversarial robustness via multi-objective optimization.

Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning. However, the existing Hierarchical Reinforcement Learning (HRL) approaches relying on symbolic reasoning are often limited as they require a manual goal representation. The challenge in autonomously discovering a symbolic goal representation is that it must preserve critical information, such as the environment dynamics. In this paper, we propose a developmental mechanism for goal discovery via an emergent representation that abstracts (i.e., groups together) sets of environment states that have similar roles in the task. We introduce a Feudal HRL algorithm that concurrently learns both the goal representation and a hierarchical policy. The algorithm uses symbolic reachability analysis for neural networks to approximate the transition relation among sets of states and to refine the goal representation. We evaluate our approach on complex navigation tasks, showing the learned representation is interpretable, transferrable and results in data efficient learning.

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. To tackle the SSDA problem on graphs, a novel method called SemiGCL is proposed, which benefits from graph contrastive learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph's local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks.

We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them.

UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put physical fitness and endurance of riders to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be estimated to ensure riders have the energy and resources necessary to maintain a high level of performance throughout a race. This task, however, is both time-consuming and challenging, as it requires precise estimates of race speed and power output. Traditionally, the approach to predicting energy needs has relied on judgement and experience of coaches, but this method has its limitations and often leads to inaccurate predictions. In this paper, we propose a new, more effective approach to predicting energy needs for cycling races. By predicting the speed and power with regression models, we provide the coaches with calorie needs estimates for each individual rider per stage instantly. In addition, we compare methods to quantify uncertainty using conformal prediction. The empirical analysis of the jackknife+, jackknife-minmax, jackknife-minmax-after-bootstrap, CV+, CV-minmax, conformalized quantile regression, and inductive conformal prediction methods in conformal prediction reveals that all methods achieve valid prediction intervals. All but minmax-based methods also produce sufficiently narrow prediction intervals for decision-making. Furthermore, methods computing prediction intervals of fixed size produce tighter intervals for low significance values. Among the methods computing intervals of varying length across the input space, inductive conformal prediction computes narrower prediction intervals at larger significance level.

In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first considers adapting multiple heterogeneous labeled source domains to an unlabeled target domain. On the other hand, the second attacks the problem of synthesizing a small summary containing all the information about the datasets. We thus consider a new problem called MSDA-DD. To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching. We thoroughly experiment with this novel problem on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous Stirred Tank Reactor, and Case Western Reserve University), where we show that, even with as little as 1 sample per class, one achieves state-of-the-art adaptation performance.

Comparing graphs by means of optimal transport has recently gained significant attention, as the distances induced by optimal transport provide both a principled metric between graphs as well as an interpretable description of the associated changes between graphs in terms of a transport plan. As the lack of symmetry introduces challenges in the typically considered formulations, optimal transport distances for graphs have mostly been developed for undirected graphs. Here, we propose two distance measures to compare directed graphs based on variants of optimal transport: (i) an earth movers distance (Wasserstein) and (ii) a Gromov-Wasserstein (GW) distance. We evaluate these two distances and discuss their relative performance for both simulated graph data and real-world directed cell-cell communication graphs, inferred from single-cell RNA-seq data.

Purpose: Common to most MRSI techniques, the spatial resolution and the minimal scan duration of Deuterium Metabolic Imaging (DMI) are limited by the achievable SNR. This work presents a deep learning method for sensitivity enhancement of DMI. Methods: A convolutional neural network (CNN) was designed to estimate the 2H-labeled metabolite concentrations from low SNR and distorted DMI FIDs. The CNN was trained with synthetic data that represent a range of SNR levels typically encountered in vivo. The estimation precision was further improved by fine-tuning the CNN with MRI-based edge-preserving regularization for each DMI dataset. The proposed processing method, PReserved Edge ConvolutIonal neural network for Sensitivity Enhanced DMI (PRECISE-DMI), was applied to simulation studies and in vivo experiments to evaluate the anticipated improvements in SNR and investigate the potential for inaccuracies. Results: PRECISE-DMI visually improved the metabolic maps of low SNR datasets, and quantitatively provided higher precision than the standard Fourier reconstruction. Processing of DMI data acquired in rat brain tumor models resulted in more precise determination of 2H-labeled lactate and glutamate + glutamine levels, at increased spatial resolution (from >8 to 2 $\mu$L) or shortened scan time (from 32 to 4 min) compared to standard acquisitions. However, rigorous SD-bias analyses showed that overuse of the edge-preserving regularization can compromise the accuracy of the results. Conclusion: PRECISE-DMI allows a flexible trade-off between enhancing the sensitivity of DMI and minimizing the inaccuracies. With typical settings, the DMI sensitivity can be improved by 3-fold while retaining the capability to detect local signal variations.

Given a set of points labeled with $k$ labels, we introduce the heat map sorting problem as reordering and merging the points and dimensions while preserving the clusters (labels). A cluster is preserved if it remains connected, i.e., if it is not split into several clusters and no two clusters are merged. We prove the problem is NP-hard and we give a fixed-parameter algorithm with a constant number of rounds in the massively parallel computation model, where each machine has a sublinear memory and the total memory of the machines is linear. We give an approximation algorithm for a NP-hard special case of the problem. We empirically compare our algorithm with k-means and density-based clustering (DBSCAN) using a dimensionality reduction via locality-sensitive hashing on several directed and undirected graphs of email and computer networks.

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about $1\%$-area edits, SIGE accelerates DDPM by $3.0\times$ on NVIDIA RTX 3090 and $4.6\times$ on Apple M1 Pro GPU, Stable Diffusion by $7.2\times$ on 3090, and GauGAN by $5.6\times$ on 3090 and $5.2\times$ on M1 Pro GPU. Compared to our conference version, we extend SIGE to accommodate attention layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results with large and sequential edits.

The goal of universal audio representation learning is to obtain foundational models that can be used for a variety of downstream tasks involving speech, music or environmental sounds. To approach this problem, methods inspired by self-supervised models from NLP, like BERT, are often used and adapted to audio. These models rely on the discrete nature of text, hence adopting this type of approach for audio processing requires either a change in the learning objective or mapping the audio signal to a set of discrete classes. In this work, we explore the use of EnCodec, a neural audio codec, to generate discrete targets for learning an universal audio model based on a masked autoencoder (MAE). We evaluate this approach, which we call EncodecMAE, on a wide range of audio tasks spanning speech, music and environmental sounds, achieving performances comparable or better than leading audio representation models.

Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noisy data, was first observed in neural network models trained with gradient descent. To better understand this empirical observation, we consider the generalization error of two-layer neural networks trained to interpolation by gradient descent on the logistic loss following random initialization. We assume the data comes from well-separated class-conditional log-concave distributions and allow for a constant fraction of the training labels to be corrupted by an adversary. We show that in this setting, neural networks exhibit benign overfitting: they can be driven to zero training error, perfectly fitting any noisy training labels, and simultaneously achieve minimax optimal test error. In contrast to previous work on benign overfitting that require linear or kernel-based predictors, our analysis holds in a setting where both the model and learning dynamics are fundamentally nonlinear.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It first leverages multivariate responses to separate marginal and uncorrelated confounding effects, recovering the confounding coefficients' column space. Subsequently, latent factors and primary effects are jointly estimated, utilizing $\ell_1$-regularization for sparsity while imposing orthogonality onto confounding coefficients. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish various effects' identification conditions and non-asymptotic error bounds. We show effective Type-I error control of asymptotic $z$-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.

Pushing the boundaries of machine learning often requires exploring different hardware and software combinations. However, the freedom to experiment across different tooling stacks can be at odds with the drive for efficiency, which has produced increasingly specialized AI hardware and incentivized consolidation around a narrow set of ML frameworks. Exploratory research can be restricted if software and hardware are co-evolving, making it even harder to stray away from mainstream ideas that work well with popular tooling stacks. While this friction increasingly impacts the rate of innovation in machine learning, to our knowledge the lack of portability in tooling has not been quantified. In this work, we ask: How portable are popular ML software frameworks? We conduct a large-scale study of the portability of mainstream ML frameworks across different hardware types. Our findings paint an uncomfortable picture -- frameworks can lose more than 40% of their key functions when ported to other hardware. Worse, even when functions are portable, the slowdown in their performance can be extreme and render performance untenable. Collectively, our results reveal how costly straying from a narrow set of hardware-software combinations can be - and suggest that specialization of hardware impedes innovation in machine learning research.

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices regarding data handling, experimental design, and model evaluation is crucial. This work summarizes and strictly observes such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. We investigate the impact of different data augmentation techniques and model complexity on the overall performance. We consider MRI data from ADNI dataset to address a classification problem employing 3D Convolutional Neural Network (CNN). The experiments are designed to compensate for data scarcity and initial random parameters by utilizing cross-validation and multiple training trials. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures, each varying in the number of convolutional layers. Specifically, the augmentation strategies are based on affine transformations, such as zoom, shift, and rotation, applied concurrently or separately. The combined effect of data augmentation and model complexity leads to a variation in prediction performance up to 10% of accuracy. When affine transformation are applied separately, the model is more accurate, independently from the adopted architecture. For all strategies, the model accuracy followed a concave behavior at increasing number of convolutional layers, peaking at an intermediate value of layers. The best model (8 CL, (B)) is the most stable across cross-validation folds and training trials, reaching excellent performance both on the testing set and on an external test set.

In this paper, we exploit the unique properties of a deterministic projected belief network (D-PBN) to take full advantage of trainable compound activation functions (TCAs). A D-PBN is a type of auto-encoder that operates by "backing up" through a feed-forward neural network. TCAs are activation functions with complex monotonic-increasing shapes that change the distribution of the data so that the linear transformation that follows is more effective. Because a D-PBN operates by "backing up", the TCAs are inverted in the reconstruction process, restoring the original distribution of the data, thus taking advantage of a given TCA in both analysis and reconstruction. In this paper, we show that a D-PBN auto-encoder with TCAs can significantly out-perform standard auto-encoders including variational auto-encoders.

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An empirical benchmark on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.

Computational pathology uses artificial intelligence to enable precision medicine and decision support systems through the analysis of whole slide images. It has the potential to revolutionize the diagnosis and treatment of cancer. However, a major challenge to this objective is that for many specific computational pathology tasks the amount of data is inadequate for development. To address this challenge, we created Virchow, a 632 million parameter deep neural network foundation model for computational pathology. Using self-supervised learning, Virchow is trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue groups, which is orders of magnitude more data than previous works. When evaluated on downstream tasks including tile-level pan-cancer detection and subtyping and slide-level biomarker prediction, Virchow outperforms state-of-the-art systems both on internal datasets drawn from the same population as the pretraining data as well as external public datasets. Virchow achieves 93% balanced accuracy for pancancer tile classification, and AUCs of 0.983 for colon microsatellite instability status prediction and 0.967 for breast CDH1 status prediction. The gains in performance highlight the importance of pretraining on massive pathology image datasets, suggesting pretraining on even larger datasets could continue improving performance for many high-impact applications where limited amounts of training data are available, such as drug outcome prediction.

Large pre-trained vision-language models such as CLIP have demonstrated great potential in zero-shot transferability to downstream tasks. However, to attain optimal performance, the manual selection of prompts is necessary to improve alignment between the downstream image distribution and the textual class descriptions. This manual prompt engineering is the major challenge for deploying such models in practice since it requires domain expertise and is extremely time-consuming. To avoid non-trivial prompt engineering, recent work Context Optimization (CoOp) introduced the concept of prompt learning to the vision domain using learnable textual tokens. While CoOp can achieve substantial improvements over manual prompts, its learned context is worse generalizable to wider unseen classes within the same dataset. In this work, we present Prompt Learning with Reparameterization Encoder (PRE) - a simple and efficient method that enhances the generalization ability of the learnable prompt to unseen classes while maintaining the capacity to learn Base classes. Instead of directly optimizing the prompts, PRE employs a prompt encoder to reparameterize the input prompt embeddings, enhancing the exploration of task-specific knowledge from few-shot samples. Experiments and extensive ablation studies on 8 benchmarks demonstrate that our approach is an efficient method for prompt learning. Specifically, PRE achieves a notable enhancement of 5.60% in average accuracy on New classes and 3% in Harmonic mean compared to CoOp in the 16-shot setting, all achieved within a good training time.

In recent studies, linear recurrent neural networks (LRNNs) have achieved Transformer-level performance in natural language modeling and long-range modeling while offering rapid parallel training and constant inference costs. With the resurged interest in LRNNs, we study whether they can learn the hidden rules in training sequences, such as the grammatical structures of regular language. We theoretically analyze some existing LRNNs and discover their limitations on regular language. Motivated by the analysis, we propose a new LRNN equipped with a block-diagonal and input-dependent transition matrix. Experiments suggest that the proposed model is the only LRNN that can perform length extrapolation on regular language tasks such as Sum, Even Pair, and Modular Arithmetic.

By identifying similarities between successive inputs, Self-Supervised Learning (SSL) methods for time series analysis have demonstrated their effectiveness in encoding the inherent static characteristics of temporal data. However, an exclusive emphasis on similarities might result in representations that overlook the dynamic attributes critical for modeling cardiovascular diseases within a confined subject cohort. Introducing Distilled Encoding Beyond Similarities (DEBS), this paper pioneers an SSL approach that transcends mere similarities by integrating dissimilarities among positive pairs. The framework is applied to electrocardiogram (ECG) signals, leading to a notable enhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib) across diverse subjects. DEBS underscores the potential of attaining a more refined representation by encoding the dynamic characteristics of time series data, tapping into dissimilarities during the optimization process. Broadly, the strategy delineated in this study holds the promise of unearthing novel avenues for advancing SSL methodologies tailored to temporal data.

Text-to-image diffusion models have achieved widespread popularity due to their unprecedented image generation capability. In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both training data augmentation and model performance assessments. In this paper, we study the efficacy and shortcomings of generative models in the context of face generation. Utilizing a combination of qualitative and quantitative measures, including embedding-based metrics and user studies, we present a framework to audit the characteristics of generated faces conditioned on a set of social attributes. We applied our framework on faces generated through state-of-the-art text-to-image diffusion models. We identify several limitations of face image generation that include faithfulness to the text prompt, demographic disparities, and distributional shifts. Furthermore, we present an analytical model that provides insights into how training data selection contributes to the performance of generative models.

The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations. In particular, the simplicial complex has inspired generalizations of graph neural networks (GNNs) to simplicial complex-based models. Learning on such systems requires large amounts of data, which can be expensive or impossible to obtain. We propose data augmentation of simplicial complexes through both linear and nonlinear mixup mechanisms that return mixtures of existing labeled samples. In addition to traditional pairwise mixup, we present a convex clustering mixup approach for a data-driven relationship among several simplicial complexes. We theoretically demonstrate that the resultant synthetic simplicial complexes interpolate among existing data with respect to homomorphism densities. Our method is demonstrated on both synthetic and real-world datasets for simplicial complex classification.

The main premise of federated learning (FL) is that machine learning model updates are computed locally to preserve user data privacy. This approach avoids by design user data to ever leave the perimeter of their device. Once the updates aggregated, the model is broadcast to all nodes in the federation. However, without proper defenses, compromised nodes can probe the model inside their local memory in search for adversarial examples, which can lead to dangerous real-world scenarios. For instance, in image-based applications, adversarial examples consist of images slightly perturbed to the human eye getting misclassified by the local model. These adversarial images are then later presented to a victim node's counterpart model to replay the attack. Typical examples harness dissemination strategies such as altered traffic signs (patch attacks) no longer recognized by autonomous vehicles or seemingly unaltered samples that poison the local dataset of the FL scheme to undermine its robustness. Pelta is a novel shielding mechanism leveraging Trusted Execution Environments (TEEs) that reduce the ability of attackers to craft adversarial samples. Pelta masks inside the TEE the first part of the back-propagation chain rule, typically exploited by attackers to craft the malicious samples. We evaluate Pelta on state-of-the-art accurate models using three well-established datasets: CIFAR-10, CIFAR-100 and ImageNet. We show the effectiveness of Pelta in mitigating six white-box state-of-the-art adversarial attacks, such as Projected Gradient Descent, Momentum Iterative Method, Auto Projected Gradient Descent, the Carlini & Wagner attack. In particular, Pelta constitutes the first attempt at defending an ensemble model against the Self-Attention Gradient attack to the best of our knowledge. Our code is available to the research community at https://github.com/queyrusi/Pelta.

This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.

Topological signal processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP via a generalized higher-order version of graphon, known as complexon. We recall the notion of a complexon as the limit of a simplicial complex sequence [1]. Inspired by the integral operator form of graphon shift operators, we construct a marginal complexon and complexon shift operator (CSO) according to components of all possible dimensions from the complexon. We investigate the CSO's eigenvalues and eigenvectors, and relate them to a new family of weighted adjacency matrices. We prove that when a simplicial complex sequence converges to a complexon, the eigenvalues of the corresponding CSOs converge to that of the limit complexon. These results hint at learning transferability on large simplicial complexes or simplicial complex sequences, which generalize the graphon signal processing framework.

In global healthcare, respiratory diseases are a leading cause of mortality, underscoring the need for rapid and accurate diagnostics. To advance rapid screening techniques via auscultation, our research focuses on employing one of the largest publicly available medical database of respiratory sounds to train multiple machine learning models able to classify different health conditions. Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to extract physiologically relevant biosignals from acoustic data, closely tied to cardiovascular and respiratory patterns, making our approach apart in its departure from conventional audio feature extraction practices. We use Power Spectral Density analysis and filtering techniques to select Intrinsic Mode Functions (IMFs) strongly correlated with underlying physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. Initially, we deploy a binary classification model that demonstrates a balanced accuracy of 87% in distinguishing between healthy and diseased individuals. Subsequently, we employ a six-class classification model that achieves a balanced accuracy of 72% in diagnosing specific respiratory conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For the first time, we also introduce regression models that estimate age and body mass index (BMI) based solely on acoustic data, as well as a model for gender classification. Our findings underscore the potential of this approach to significantly enhance assistive and remote diagnostic capabilities.

Unsupervised contrastive learning methods have recently seen significant improvements, particularly through data augmentation strategies that aim to produce robust and generalizable representations. However, prevailing data augmentation methods, whether hand designed or based on foundation models, tend to rely heavily on prior knowledge or external data. This dependence often compromises their effectiveness and efficiency. Furthermore, the applicability of most existing data augmentation strategies is limited when transitioning to other research domains, especially science-related data. This limitation stems from the paucity of prior knowledge and labeled data available in these domains. To address these challenges, we introduce DiffAug-a novel and efficient Diffusion-based data Augmentation technique. DiffAug aims to ensure that the augmented and original data share a smoothed latent space, which is achieved through diffusion steps. Uniquely, unlike traditional methods, DiffAug first mines sufficient prior semantic knowledge about the neighborhood. This provides a constraint to guide the diffusion steps, eliminating the need for labels, external data/models, or prior knowledge. Designed as an architecture-agnostic framework, DiffAug provides consistent improvements. Specifically, it improves image classification and clustering accuracy by 1.6%~4.5%. When applied to biological data, DiffAug improves performance by up to 10.1%, with an average improvement of 5.8%. DiffAug shows good performance in both vision and biological domains.

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.

Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques. In this work, we introduce GOKU-UI, an evolution of the SciML generative model GOKU-nets. GOKU-UI not only broadens the original model's spectrum to incorporate other classes of differential equations, such as Stochastic Differential Equations (SDEs), but also integrates attention mechanisms and a novel multiple shooting training strategy in the latent space. These modifications have led to a significant increase in its performance in both reconstruction and forecast tasks, as demonstrated by our evaluation of simulated and empirical data. Specifically, GOKU-UI outperformed all baseline models on synthetic datasets even with a training set 16-fold smaller, underscoring its remarkable data efficiency. Furthermore, when applied to empirical human brain data, while incorporating stochastic Stuart-Landau oscillators into its dynamical core, our proposed enhancements markedly increased the model's effectiveness in capturing complex brain dynamics. This augmented version not only surpassed all baseline methods in the reconstruction task, but also demonstrated lower prediction error of future brain activity up to 15 seconds ahead. By training GOKU-UI on resting state fMRI data, we encoded whole-brain dynamics into a latent representation, learning a low-dimensional dynamical system model that could offer insights into brain functionality and open avenues for practical applications such as the classification of mental states or psychiatric conditions. Ultimately, our research provides further impetus for the field of Scientific Machine Learning, showcasing the potential for advancements when established scientific insights are interwoven with modern machine learning.

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.

Many complex real world phenomena exhibit abrupt, intermittent or jumping behaviors, which are more suitable to be described by stochastic differential equations under non-Gaussian L\'evy noise. Among these complex phenomena, the most likely transition paths between metastable states are important since these rare events may have a high impact in certain scenarios. Based on the large deviation principle, the most likely transition path could be treated as the minimizer of the rate function upon paths that connect two points. One of the challenges to calculate the most likely transition path for stochastic dynamical systems under non-Gaussian L\'evy noise is that the associated rate function can not be explicitly expressed by paths. For this reason, we formulate an optimal control problem to obtain the optimal state as the most likely transition path. We then develop a neural network method to solve this issue. Several experiments are investigated for both Gaussian and non-Gaussian cases.

Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs to classify and reconstruct images from the ImageNet dataset using electroencephalography (EEG) data from subjects that had viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6 participants, each exposed to 50 images spanning 40 unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 80%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for e.g. swift, individualized feedback experiments. Our research represents a significant step forward in connecting neural signals with visual cognition.

The integration of manipulator robots in household environments suggests a need for more predictable and human-like robot motion. This holds especially true for wheelchair-mounted assistive robots that can support the independence of people with paralysis. One method of generating naturalistic motion trajectories is via the imitation of human demonstrators. This paper explores a self-supervised imitation learning method using an autoregressive spatio-temporal graph neural network for an assistive drinking task. We address learning from diverse human motion trajectory data that were captured via wearable IMU sensors on a human arm as the action-free task demonstrations. Observed arm motion data from several participants is used to generate natural and functional drinking motion trajectories for a UR5e robot arm.

Quantile Regression (QR) can be used to estimate aleatoric uncertainty in deep neural networks and can generate prediction intervals. Quantifying uncertainty is particularly important in critical applications such as clinical diagnosis, where a realistic assessment of uncertainty is essential in determining disease status and planning the appropriate treatment. The most common application of quantile regression models is in cases where the parametric likelihood cannot be specified. Although quantile regression is quite robust to outlier response observations, it can be sensitive to outlier covariate observations (features). Outlier features can compromise the performance of deep learning regression problems such as style translation, image reconstruction, and deep anomaly detection, potentially leading to misleading conclusions. To address this problem, we propose a robust solution for quantile regression that incorporates concepts from robust divergence. We compare the performance of our proposed method with (i) least trimmed quantile regression and (ii) robust regression based on the regularization of case-specific parameters in a simple real dataset in the presence of outlier. These methods have not been applied in a deep learning framework. We also demonstrate the applicability of the proposed method by applying it to a medical imaging translation task using diffusion models.

Human activity recognition (HAR) is an essential research field that has been used in different applications including home and workplace automation, security and surveillance as well as healthcare. Starting from conventional machine learning methods to the recently developing deep learning techniques and the Internet of things, significant contributions have been shown in the HAR area in the last decade. Even though several review and survey studies have been published, there is a lack of sensor-based HAR overview studies focusing on summarising the usage of wearable sensors and smart home sensors data as well as applications of HAR and deep learning techniques. Hence, we overview sensor-based HAR, discuss several important applications that rely on HAR, and highlight the most common machine learning methods that have been used for HAR. Finally, several challenges of HAR are explored that should be addressed to further improve the robustness of HAR.

A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold.

One of the common human diseases is sleep disorders. The classification of sleep stages plays a fundamental role in diagnosing sleep disorders, monitoring treatment effectiveness, and understanding the relationship between sleep stages and various health conditions. A precise and efficient classification of these stages can significantly enhance our understanding of sleep-related phenomena and ultimately lead to improved health outcomes and disease treatment. Models others propose are often time-consuming and lack sufficient accuracy, especially in stage N1. The main objective of this research is to present a machine-learning model called "EEGMobile". This model utilizes pre-trained models and learns from electroencephalogram (EEG) spectrograms of brain signals. The model achieved an accuracy of 86.97% on a publicly available dataset named "Sleep-EDF20", outperforming other models proposed by different researchers. Moreover, it recorded an accuracy of 56.4% in stage N1, which is better than other models. These findings demonstrate that this model has the potential to achieve better results for the treatment of this disease.

Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits. Current physical realizations of qubits require a careful calibration, composed by different experiments, due to noise and decoherence phenomena. Among the different characterization experiments, a crucial step is to develop a model to classify the measured state by discriminating the ground state from the excited state. In this proceedings we benchmark multiple classification techniques applied to real quantum devices.

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated, but for now, it is not comprehensive for wearable devices to recognize and analyze the movement of specific sports. Based on this, this paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence. Firstly, a device is designed to collect the movement information of table tennis players and the actual movement data is processed. Secondly, a sliding window is made to divide the collected motion data into a characteristic database of six table tennis benchmark movements. Thirdly, motion features were constructed based on feature engineering, and motor skills were identified for different models after dimensionality reduction. Finally, the hierarchical evaluation system of motor skills is established with the loss functions of different evaluation indexes. The results show that in the recognition of table tennis players' motor skills, the feature-based BP neural network proposed in this paper has higher recognition accuracy and stronger generalization ability than the traditional convolutional neural network.

Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its NP- hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder, named DREIM. Trough extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem.

Sleep, a fundamental physiological process, occupies a significant portion of our lives. Accurate classification of sleep stages serves as a crucial tool for evaluating sleep quality and identifying probable sleep disorders. This work introduces a novel methodology that utilises a SE-Resnet-Bi-LSTM architecture to classify sleep into five separate stages. The classification process is based on the analysis of single-channel electroencephalograms (EEGs). The framework that has been suggested consists of two fundamental elements: a feature extractor that utilises SE-ResNet, and a temporal context encoder that use stacks of Bi-LSTM units.The effectiveness of our approach is substantiated by thorough assessments conducted on three different datasets, namely SLeepEDF-20, SleepEDF-78, and SHHS. Significantly, our methodology attains notable levels of accuracy, specifically 87.5\%, 83.9\%, and 87.8\%, along with macro-F1 scores of 82.5, 78.9, and 81.9 for the corresponding datasets. Notably, we introduce the utilization of 1D-GradCAM visualization to shed light on the decision-making process of our model in the realm of sleep stage classification. This visualization method not only provides valuable insights into the model's classification rationale but also aligns its outcomes with the annotations made by sleep experts. One notable feature of our research is the integration of an expedited training approach, which effectively preserves the model's resilience in terms of performance. The experimental evaluations conducted provide a comprehensive evaluation of the effectiveness of our proposed model in comparison to existing approaches, highlighting its potential for practical applications.

This paper proposes a direct text to speech translation system using discrete acoustic units. This framework employs text in different source languages as input to generate speech in the target language without the need for text transcriptions in this language. Motivated by the success of acoustic units in previous works for direct speech to speech translation systems, we use the same pipeline to extract the acoustic units using a speech encoder combined with a clustering algorithm. Once units are obtained, an encoder-decoder architecture is trained to predict them. Then a vocoder generates speech from units. Our approach for direct text to speech translation was tested on the new CVSS corpus with two different text mBART models employed as initialisation. The systems presented report competitive performance for most of the language pairs evaluated. Besides, results show a remarkable improvement when initialising our proposed architecture with a model pre-trained with more languages.

Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as generating meshes, arranging geometry, synthesizing textures, etc. In this paper we develop a model to generate Bidirectional Reflectance Distribution Functions (BRDFs) from descriptive textual prompts. BRDFs are four dimensional probability distributions that characterize the interaction of light with surface materials. They are either represented parametrically, or by tabulating the probability density associated with every pair of incident and outgoing angles. The former lends itself to artistic editing while the latter is used when measuring the appearance of real materials. Numerous works have focused on hypothesizing BRDF models from images of materials. We learn a mapping from textual descriptions of materials to parametric BRDFs. Our model is first trained using a semi-supervised approach before being tuned via an unsupervised scheme. Although our model is general, in this paper we specifically generate parameters for MDL materials, conditioned on natural language descriptions, within NVIDIA's Omniverse platform. This enables use cases such as real-time text prompts to change materials of objects in 3D environments such as "dull plastic" or "shiny iron". Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions.

In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks. However, deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. This paper provides an overview of model reprogramming to bridge this gap. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing and reusing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning, where the source and target domains can be vastly different. In many applications, model reprogramming outperforms transfer learning and training from scratch. This paper elucidates the methodology of model reprogramming, summarizes existing use cases, provides a theoretical explanation of the success of model reprogramming, and concludes with a discussion on open-ended research questions and opportunities. A list of model reprogramming studies is actively maintained and updated at https://github.com/IBM/model-reprogramming.

The discovery of governing equations from data has been an active field of research for decades. One widely used methodology for this purpose is sparse regression for nonlinear dynamics, known as SINDy. Despite several attempts, noisy and scarce data still pose a severe challenge to the success of the SINDy approach. In this work, we discuss a robust method to discover nonlinear governing equations from noisy and scarce data. To do this, we make use of neural networks to learn an implicit representation based on measurement data so that not only it produces the output in the vicinity of the measurements but also the time-evolution of output can be described by a dynamical system. Additionally, we learn such a dynamic system in the spirit of the SINDy framework. Leveraging the implicit representation using neural networks, we obtain the derivative information -- required for SINDy -- using an automatic differentiation tool. To enhance the robustness of our methodology, we further incorporate an integral condition on the output of the implicit networks. Furthermore, we extend our methodology to handle data collected from multiple initial conditions. We demonstrate the efficiency of the proposed methodology to discover governing equations under noisy and scarce data regimes by means of several examples and compare its performance with existing methods.

Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics whose energy consumption depends on the number of spikes exchanged between neurons over the course of the input presentation. In typical implementations of an SNN classifier, decisions are produced after the entire input sequence has been processed, resulting in latency and energy consumption levels that are fairly uniform across inputs. Recently introduced delay-adaptive SNNs tailor the inference latency -- and, with it, the energy consumption -- to the difficulty of each example, by producing an early decision when the SNN model is sufficiently ``confident''. In this paper, we start by observing that, as an SNN processes input samples, its classification decisions tend to be first under-confident and then over-confident with respect to the decision's ground-truth, unknown, test accuracy. This makes it difficult to determine a stopping time that ensures a desired level of accuracy. To address this problem, we introduce a novel delay-adaptive SNN-based inference methodology that, wrapping around any pre-trained SNN classifier, provides guaranteed reliability for the decisions produced at input-dependent stopping times. The approach entails minimal added complexity as compared to the underlying SNN, requiring only thresholding and counting operations at run time, and it leverages tools from conformal prediction (CP).

While the recently introduced Tree of Thoughts (ToT) has heralded advancements in allowing Large Language Models (LLMs) to reason through foresight and backtracking for global decision-making, it has overlooked the inherent local uncertainties in intermediate decision points or "thoughts". These local uncertainties, intrinsic to LLMs given their potential for diverse responses, remain a significant concern in the reasoning process. Addressing this pivotal gap, we introduce the Tree of Uncertain Thoughts (TouT) - a reasoning framework tailored for LLMs. Our TouT effectively leverages Monte Carlo Dropout to quantify uncertainty scores associated with LLMs' diverse local responses at these intermediate steps. By marrying this local uncertainty quantification with global search algorithms, TouT enhances the model's precision in response generation. We substantiate our approach with rigorous experiments on two demanding planning tasks: Game of 24 and Mini Crosswords. The empirical evidence underscores TouT's superiority over both ToT and chain-of-thought prompting methods.

We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment. Specifically, we aim to improve the agent's ability to generalize to out-of-distribution goals. To achieve this, we propose to learn a dynamics model and check if it is equivariant with respect to a fixed type of transformation, namely translations in the state space. We then use an entropy regularizer to increase the equivariant set and augment the dataset with the resulting transformed samples. Finally, we learn a new policy offline based on the augmented dataset, with an off-the-shelf offline RL algorithm. Our experimental results demonstrate that our approach can greatly improve the test performance of the policy on the considered environments.

Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential attacks makes detecting adversarial examples challenging. Additionally, attackers do not need to follow the rules made by the defender. To address this problem, we take inspiration from the concept of Applicability Domain in cheminformatics. Cheminformatics models struggle to make accurate predictions because only a limited number of compounds are known and available for training. Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain. Similarly, adversarial examples start as harmless inputs, but can be manipulated to evade reliable classification by moving outside the domain of the classifier. We are the first to identify the similarity between Applicability Domain and adversarial detection. Instead of focusing on unknown attacks, we focus on what is known, the training data. We propose a simple yet robust triple-stage data-driven framework that checks the input globally and locally, and confirms that they are coherent with the model's output. This framework can be applied to any classification model and is not limited to specific attacks. We demonstrate these three stages work as one unit, effectively detecting various attacks, even for a white-box scenario.

In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations. This gives rise to the joint problem of community detection and synchronization. We propose a series of semidefinite relaxations, and prove their exact recovery when extending the celebrated stochastic block model to this new setting where both rotations and cluster identities are to be determined. Numerical experiments demonstrate the efficacy of our proposed algorithms and confirm our theoretical result which indicates a sharp phase transition for exact recovery.

Languages are not created randomly but rather to communicate information. There is a strong association between languages and their underlying meanings, resulting in a sparse joint distribution that is heavily peaked according to their correlations. Moreover, these peak values happen to match with the marginal distribution of languages due to the sparsity. With the advent of LLMs trained on big data and large models, we can now precisely assess the marginal distribution of languages, providing a convenient means of exploring the sparse structures in the joint distribution for effective inferences. In this paper, we categorize languages as either unambiguous or {\epsilon}-ambiguous and present quantitative results to demonstrate that the emergent abilities of LLMs, such as language understanding, in-context learning, chain-of-thought prompting, and effective instruction fine-tuning, can all be attributed to Bayesian inference on the sparse joint distribution of languages.

In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions. In our setting, clients' distributions represent particular domains, and FedDaDiL collectively trains a federated dictionary of empirical distributions. In particular, we build upon the Dataset Dictionary Learning framework by designing collaborative communication protocols and aggregation operations. The chosen protocols keep clients' data private, thus enhancing overall privacy compared to its centralized counterpart. We empirically demonstrate that our approach successfully generates labeled data on the target domain with extensive experiments on (i) Caltech-Office, (ii) TEP, and (iii) CWRU benchmarks. Furthermore, we compare our method to its centralized counterpart and other benchmarks in federated domain adaptation.

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

We show that unless P=NP, there cannot be a polynomial-time algorithm that finds a point within Euclidean distance $c^n$ (for any constant $c \ge 0$) of a local minimizer of an $n$-variate quadratic function over a polytope. This result (even with $c=0$) answers a question of Pardalos and Vavasis that appeared in 1992 on a list of seven open problems in complexity theory for numerical optimization. Our proof technique also implies that the problem of deciding whether a quadratic function has a local minimizer over an (unbounded) polyhedron, and that of deciding if a quartic polynomial has a local minimizer are NP-hard.

Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge, especially when encountering noisy observations and no prior knowledge available. This study proposes R-DISCOVER, a framework designed to robustly uncover open-form partial differential equations (PDEs) from limited and noisy data. The framework operates through two alternating update processes: discovering and embedding. The discovering phase employs symbolic representation and a reinforcement learning (RL)-guided hybrid PDE generator to efficiently produce diverse open-form PDEs with tree structures. A neural network-based predictive model fits the system response and serves as the reward evaluator for the generated PDEs. PDEs with superior fits are utilized to iteratively optimize the generator via the RL method and the best-performing PDE is selected by a parameter-free stability metric. The embedding phase integrates the initially identified PDE from the discovering process as a physical constraint into the predictive model for robust training. The traversal of PDE trees automates the construction of the computational graph and the embedding process without human intervention. Numerical experiments demonstrate our framework's capability to uncover governing equations from nonlinear dynamic systems with limited and highly noisy data and outperform other physics-informed neural network-based discovery methods. This work opens new potential for exploring real-world systems with limited understanding.

Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are seldom-studied. Compared to CNNs capturing pixel-wise local features by convolutions, ViTs extract global context information through patches and attentions. Na\"ively transplanting CNN-specific backdoor attacks to ViTs yields only a low clean data accuracy and a low attack success rate. In this paper, we propose a stealth and practical ViT-specific backdoor attack $TrojViT$. Rather than an area-wise trigger used by CNN-specific backdoor attacks, TrojViT generates a patch-wise trigger designed to build a Trojan composed of some vulnerable bits on the parameters of a ViT stored in DRAM memory through patch salience ranking and attention-target loss. TrojViT further uses minimum-tuned parameter update to reduce the bit number of the Trojan. Once the attacker inserts the Trojan into the ViT model by flipping the vulnerable bits, the ViT model still produces normal inference accuracy with benign inputs. But when the attacker embeds a trigger into an input, the ViT model is forced to classify the input to a predefined target class. We show that flipping only few vulnerable bits identified by TrojViT on a ViT model using the well-known RowHammer can transform the model into a backdoored one. We perform extensive experiments of multiple datasets on various ViT models. TrojViT can classify $99.64\%$ of test images to a target class by flipping $345$ bits on a ViT for ImageNet.Our codes are available at https://github.com/mxzheng/TrojViT

The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algorithms to optimize radio access network (RAN) functions via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are among the main approaches adopted in the O-RAN literature to solve dynamic radio resource management problems. However, despite the benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms in real network deployments falls behind. This is primarily due to the slow convergence and unstable performance exhibited by DRL agents upon deployment and when facing previously unseen network conditions. In this paper, we address these challenges by proposing transfer learning (TL) as a core component of the training and deployment workflows for the DRL-based closed-loop control of O-RAN functionalities. To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates multiple services, including real VR gaming traffic to reflect practical scenarios of O-RAN slicing. We also propose and implement policy reuse and distillation-aided DRL and non-TL-aided DRL as three separate baselines. The proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the average initial reward value and the percentage of converged scenarios, and a 64.6% decrease in reward variance while maintaining fast convergence and enhancing the generalizability compared with the baselines.

The task of blind source separation (BSS) involves separating sources from a mixture without prior knowledge of the sources or the mixing system. This is a challenging problem that often requires making restrictive assumptions about both the mixing system and the sources. In this paper, we propose a novel method for addressing BSS of non-linear mixtures by leveraging the natural feature subspace specialization ability of multi-encoder autoencoders with fully self-supervised learning without strong priors. During the training phase, our method unmixes the input into the separate encoding spaces of the multi-encoder network and then remixes these representations within the decoder for a reconstruction of the input. Then to perform source inference, we introduce a novel encoding masking technique whereby masking out all but one of the encodings enables the decoder to estimate a source signal. To this end, we also introduce a so-called pathway separation loss that encourages sparsity between the unmixed encoding spaces throughout the decoder's layers and a so-called zero reconstruction loss on the decoder for coherent source estimations. In order to carefully evaluate our method, we conduct experiments on a toy dataset and with real-world biosignal recordings from a polysomnography sleep study for extracting respiration.

In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior, which is known as posterior collapse and hinders the quality of representation learning. To mitigate this problem, an adjustable hyperparameter beta has been introduced in the VAE. This paper presents a closed-form expression to assess the relationship between the beta in VAE, the dataset size, the posterior collapse, and the rate-distortion curve by analyzing a minimal VAE in a high-dimensional limit. These results clarify that a long plateau in the generalization error emerges with a relatively larger beta. As the beta increases, the length of the plateau extends and then becomes infinite beyond a certain beta threshold. This implies that the choice of beta, unlike the usual regularization parameters, can induce posterior collapse regardless of the dataset size. Thus, beta is a risky parameter that requires careful tuning. Furthermore, considering the dataset-size dependence on the rate-distortion curve, a relatively large dataset is required to obtain a rate-distortion curve with high rates. Extensive numerical experiments support our analysis.

Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages. A notable development is through quantum recurrent neural networks (QRNNs) for memory-intensive tasks such as partially observable environments. However, QRL models incorporating QRNN encounter challenges such as inefficient training of QRL with QRNN, given that the computation of gradients in QRNN is both computationally expensive and time-consuming. This work presents a novel approach to address this challenge by constructing QRL agents utilizing QRNN-based reservoirs, specifically employing quantum long short-term memory (QLSTM). QLSTM parameters are randomly initialized and fixed without training. The model is trained using the asynchronous advantage actor-aritic (A3C) algorithm. Through numerical simulations, we validate the efficacy of our QLSTM-Reservoir RL framework. Its performance is assessed on standard benchmarks, demonstrating comparable results to a fully trained QLSTM RL model with identical architecture and training settings.

We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits. In particular, we utilize the Okounkov-Vershik approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the equivalence between $\operatorname{SU}(d)$ and $S_n$ irrep bases and to establish the $S_n$-equivariant Convolutional Quantum Alternating Ans\"atze ($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA is able to generate any unitary in any given $S_n$ irrep sector, which may serve as a universal model for a wide array of quantum machine learning problems with the presence of SU($d$) symmetry. Our method provides another way to prove the universality of Quantum Approximate Optimization Algorithm (QAOA) and verifies that 4-local SU($d$) symmetric unitaries are sufficient to build generic SU($d$) symmetric quantum circuits up to relative phase factors. We present numerical simulations to showcase the effectiveness of the ans\"atze to find the ground state energy of the $J_1$--$J_2$ antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our work provides the first application of the celebrated Okounkov-Vershik's $S_n$ representation theory to quantum physics and machine learning, from which to propose quantum variational ans\"atze that strongly suggests to be classically intractable tailored towards a specific optimization problem.

This paper aims at the algorithmic/theoretical core of reinforcement learning (RL) by introducing the novel class of proximal Bellman mappings. These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they are shown to belong to the powerful Hilbertian family of (firmly) nonexpansive mappings, regardless of the values of their discount factors, and possess ample degrees of design freedom to even reproduce attributes of the classical Bellman mappings and to pave the way for novel RL designs. An approximate policy-iteration scheme is built on the proposed class of mappings to solve the problem of selecting online, at every time instance, the "optimal" exponent $p$ in a $p$-norm loss to combat outliers in linear adaptive filtering, without training data and any knowledge on the statistical properties of the outliers. Numerical tests on synthetic data showcase the superior performance of the proposed framework over several non-RL and kernel-based RL schemes.

Accelerating the learning of Partial Differential Equations (PDEs) from experimental data will speed up the pace of scientific discovery. Previous randomized algorithms exploit sparsity in PDE updates for acceleration. However such methods are applicable to a limited class of decomposable PDEs, which have sparse features in the value domain. We propose Reel, which accelerates the learning of PDEs via random projection and has much broader applicability. Reel exploits the sparsity by decomposing dense updates into sparse ones in both the value and frequency domains. This decomposition enables efficient learning when the source of the updates consists of gradually changing terms across large areas (sparse in the frequency domain) in addition to a few rapid updates concentrated in a small set of "interfacial" regions (sparse in the value domain). Random projection is then applied to compress the sparse signals for learning. To expand the model applicability, Taylor series expansion is used in Reel to approximate the nonlinear PDE updates with polynomials in the decomposable form. Theoretically, we derive a constant factor approximation between the projected loss function and the original one with poly-logarithmic number of projected dimensions. Experimentally, we provide empirical evidence that our proposed Reel can lead to faster learning of PDE models (70-98% reduction in training time when the data is compressed to 1% of its original size) with comparable quality as the non-compressed models.

A rising trend in theoretical deep learning is to understand why deep learning works through Neural Tangent Kernel (NTK) [jgh18], a kernel method that is equivalent to using gradient descent to train a multi-layer infinitely-wide neural network. NTK is a major step forward in the theoretical deep learning because it allows researchers to use traditional mathematical tools to analyze properties of deep neural networks and to explain various neural network techniques from a theoretical view. A natural extension of NTK on graph learning is \textit{Graph Neural Tangent Kernel (GNTK)}, and researchers have already provide GNTK formulation for graph-level regression and show empirically that this kernel method can achieve similar accuracy as GNNs on various bioinformatics datasets [dhs+19]. The remaining question now is whether solving GNTK regression is equivalent to training an infinite-wide multi-layer GNN using gradient descent. In this paper, we provide three new theoretical results. First, we formally prove this equivalence for graph-level regression. Second, we present the first GNTK formulation for node-level regression. Finally, we prove the equivalence for node-level regression.

This paper studies convergence rates for some value function approximations that arise in a collection of reproducing kernel Hilbert spaces (RKHS) $H(\Omega)$. By casting an optimal control problem in a specific class of native spaces, strong rates of convergence are derived for the operator equation that enables offline approximations that appear in policy iteration. Explicit upper bounds on error in value function approximations are derived in terms of power function $\Pwr_{H,N}$ for the space of finite dimensional approximants $H_N$ in the native space $H(\Omega)$. These bounds are geometric in nature and refine some well-known, now classical results concerning convergence of approximations of value functions.

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing methods usually resort to analyzing the relationship of various seen classes residing in a sample from the dimension of spatial or semantic characteristics, and transfer the learned model to unseen ones. But they ignore the effective integration of local and global features. That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range. This integrated neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and effective group bi-enhancement framework for MLZSL, dubbed GBE-MLZSL, to fully make use of such properties and enable a more accurate and robust visual-semantic projection. Specifically, we split the feature maps into several feature groups, of which each feature group can be trained independently with the Local Information Distinguishing Module (LID) to ensure uniqueness. Meanwhile, a Global Enhancement Module (GEM) is designed to preserve the principal direction. Besides, a static graph structure is designed to construct the correlation of local features. Experiments on large-scale MLZSL benchmark datasets NUS-WIDE and Open-Images-v4 demonstrate that the proposed GBE-MLZSL outperforms other state-of-the-art methods with large margins.

In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classification task in the ECG domain. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports. The proposed framework excels in both linear evaluation and zero-shot classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets, showcasing its ability for robust and generalizable cross-modal ECG feature learning.

Partial differential equations (PDEs) are used to describe a variety of physical phenomena. Often these equations do not have analytical solutions and numerical approximations are used instead. One of the common methods to solve PDEs is the finite element method. Computing derivative information of the solution with respect to the input parameters is important in many tasks in scientific computing. We extend JAX automatic differentiation library with an interface to Firedrake finite element library. High-level symbolic representation of PDEs allows bypassing differentiating through low-level possibly many iterations of the underlying nonlinear solvers. Differentiating through Firedrake solvers is done using tangent-linear and adjoint equations. This enables the efficient composition of finite element solvers with arbitrary differentiable programs. The code is available at github.com/IvanYashchuk/jax-firedrake.

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic differentiation. In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations. Rather, Gibbs-Duhem consistency serves as regularization, with the flexibility of ML models being preserved. Our results show increased thermodynamic consistency and generalization capabilities for activity coefficient predictions by Gibbs-Duhem-informed graph neural networks and matrix completion methods. We also find that the model architecture, particularly the activation function, can have a strong influence on the prediction quality. The approach can be easily extended to account for other thermodynamic consistency conditions.

MELAGE, a pioneering Python-based neuroimaging software, emerges as a versatile tool for the visualization, processing, and analysis of medical images. Initially conceived to address the unique challenges of processing 3D ultrasound and MRI brain images during the neonatal period, MELAGE exhibits remarkable adaptability, extending its utility to the domain of adult human brain imaging. At its core, MELAGE features a semi-automatic brain extraction tool empowered by a deep learning module, ensuring precise and efficient brain structure extraction from MRI and 3D Ultrasound data. Moreover, MELAGE offers a comprehensive suite of features, encompassing dynamic 3D visualization, accurate measurements, and interactive image segmentation. This transformative software holds immense promise for researchers and clinicians, offering streamlined image analysis, seamless integration with deep learning algorithms, and broad applicability in the realm of medical imaging.

Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset.

Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-life conditions, comfortable and smart wearable devices with long battery life are required, which in turn set the demand for resource-constrained and energy-efficient computing solutions. In this context, the development of machine learning algorithms for seizure detection faces the challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new lightweight seizure detection network, and Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The proposed EpiDeNet-SSWCE approach demonstrates the successful detection of 91.16% and 92.00% seizure events on two different datasets (CHB-MIT and PEDESITE, respectively), with only four EEG channels. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for implementation on low-power embedded platforms, and we evaluate its performance on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power (PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best ARM Cortex-based solutions by approximately 160x in energy efficiency. The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.

The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection systems. However, traditional security systems are designed with a closed-world perspective and often face challenges in dealing with the ever-evolving threat landscape, where new and unfamiliar attacks are constantly emerging. In this paper, we introduce a framework aimed at mitigating the open set recognition (OSR) problem in the realm of Network Intrusion Detection Systems (NIDS) tailored for IoT environments. Our framework capitalizes on image-based representations of packet-level data, extracting spatial and temporal patterns from network traffic. Additionally, we integrate stacking and sub-clustering techniques, enabling the identification of unknown attacks by effectively modeling the complex and diverse nature of benign behavior. The empirical results prominently underscore the framework's efficacy, boasting an impressive 88\% detection rate for previously unseen attacks when compared against existing approaches and recent advancements. Future work will perform extensive experimentation across various openness levels and attack scenarios, further strengthening the adaptability and performance of our proposed solution in safeguarding IoT environments.

In many randomized experiments, the treatment effect of the long-term metric (i.e. the primary outcome of interest) is often difficult or infeasible to measure. Such long-term metrics are often slow to react to changes and sufficiently noisy they are challenging to faithfully estimate in short-horizon experiments. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric -- so they can be used to effectively guide decision-making in the near-term. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments. Our procedure first reduces the construction of an optimal proxy metric in a given experiment to a portfolio optimization problem which depends on the true latent treatment effects and noise level of experiment under consideration. We then denoise the observed treatment effects of the long-term metric and a set of proxies in a historical corpus of randomized experiments to extract estimates of the latent treatment effects for use in the optimization problem. One key insight derived from our approach is that the optimal proxy metric for a given experiment is not apriori fixed; rather it should depend on the sample size (or effective noise level) of the randomized experiment for which it is deployed. To instantiate and evaluate our framework, we employ our methodology in a large corpus of randomized experiments from an industrial recommendation system and construct proxy metrics that perform favorably relative to several baselines.

As one of the important tools for spatial feature extraction, graph convolution has been applied in a wide range of fields such as traffic flow prediction. However, current popular works of graph convolution cannot guarantee spatio-temporal consistency in a long period. The ignorance of correlational dynamics, convolutional locality and temporal comprehensiveness would limit predictive accuracy. In this paper, a novel Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve traffic flow prediction in highway transportation. Three temporal resolutions of data sequence are effectively integrated by self-attention to extract characteristics; multi-dynamic graphs and their weights are dynamically created to compliantly combine the varying characteristics; a dedicated gated kernel emphasizing highly relative nodes is introduced on these complete graphs to reduce overfitting for graph convolution operations. Experiments on two public datasets show our work better than state-of-the-art baselines, and case studies of a real Web system prove practical benefit in highway transportation.

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration, modified feedback, in which we applied a hidden augmentation of error to these probabilities, and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that, relative to baseline, the modified feedback condition led to significantly improved accuracy and improved gesture class separation. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.

In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. In these same domains, there may be heterogeneity both in who responds in taking-up treatment, and heterogeneity in treatment efficacy. While optimal treatment rules can maximize causal outcomes across the population, access parity constraints or other fairness considerations can be relevant in the case of encouragement. For example, in social services, a persistent puzzle is the gap in take-up of beneficial services among those who may benefit from them the most. When in addition the decision-maker has distributional preferences over both access and average outcomes, the optimal decision rule changes. We study causal identification, statistical variance-reduced estimation, and robust estimation of optimal treatment rules, including under potential violations of positivity. We consider fairness constraints such as demographic parity in treatment take-up, and other constraints, via constrained optimization. Our framework can be extended to handle algorithmic recommendations under an often-reasonable covariate-conditional exclusion restriction, using our robustness checks for lack of positivity in the recommendation. We develop a two-stage algorithm for solving over parametrized policy classes under general constraints to obtain variance-sensitive regret bounds. We illustrate the methods in two case studies based on data from randomized encouragement to enroll in insurance and from pretrial supervised release with electronic monitoring.

Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method. Inspired by the U-net architecture, DSC utilizes an autoencoder integrating CNN-RNN layers to learn latent representations of the spatiotemporal data. DSC also includes a unique layer for cluster assignment on latent representations that uses the Student's t-distribution. By optimizing the clustering loss and data reconstruction loss simultaneously, the algorithm gradually improves clustering assignments and the nonlinear mapping between low-dimensional latent feature space and high-dimensional original data space. A multivariate spatiotemporal climate dataset is used to evaluate the efficacy of the proposed method. Our extensive experiments show our approach outperforms both conventional and deep learning-based unsupervised clustering algorithms. Additionally, we compared the proposed model with its various variants (CNN encoder, CNN autoencoder, CNN-RNN encoder, CNN-RNN autoencoder, etc.) to get insight into using both the CNN and RNN layers in the autoencoder, and our proposed technique outperforms these variants in terms of clustering results.

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.  ( 2 min )

Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally-inefficient and memory-hungry; bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. TargetCall aims to filter out all off-target reads before basecalling. The highly-accurate but slow basecalling is performed only on the raw signals whose noisy reads are labeled as on-target. Our thorough experimental evaluations using both real and simulated data show that TargetCall 1) improves the end-to-end basecalling performance while maintaining high sensitivity in keeping on-target reads, 2) maintains high accuracy in downstream analysis, 3) precisely filters out up to 94.71% of off-target reads, and 4) achieves better performance, throughput, sensitivity, precision, and generality compared to prior works. We open-source TargetCall at https://github.com/CMU-SAFARI/TargetCall  ( 3 min )

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that utilizes the implicit clustering of the Nearest Neighbor Contrastive Learning (NNCLR) objective to induce abstraction in the topmost layers of a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Notably, MAE-CT does not rely on hand-crafted augmentations and frequently achieves its best performances while using only minimal augmentations (crop & flip). Further, MAE-CT is compute efficient as it requires at most 10% overhead compared to MAE re-training. Applied to large and huge Vision Transformer (ViT) models, MAE-CT excels over previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. With ViT-H/16 MAE-CT achieves a new state-of-the-art in linear probing of 82.2%.  ( 3 min )

Variational algorithms require architectures that naturally constrain the optimisation space to run efficiently. In geometric quantum machine learning, one achieves this by encoding group structure into parameterised quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge. In this paper, we propose the use of spin networks, a form of directed tensor network invariant under a group transformation, to devise SU(2) equivariant quantum circuit ans\"atze -- circuits possessing spin rotation symmetry. By changing to the basis that block diagonalises SU(2) group action, these networks provide a natural building block for constructing parameterised equivariant quantum circuits. We prove that our construction is mathematically equivalent to other known constructions, such as those based on twirling and generalised permutations, but more direct to implement on quantum hardware. The efficacy of our constructed circuits is tested by solving the ground state problem of SU(2) symmetric Heisenberg models on the one-dimensional triangular lattice and on the Kagome lattice. Our results highlight that our equivariant circuits boost the performance of quantum variational algorithms, indicating broader applicability to other real-world problems.  ( 2 min )

Meteorological radar reflectivity data (i.e. radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex Numerical Weather Prediction (NWP) models. In comparison to conventional models, Deep Learning (DL)-based radar echo extrapolation algorithms exhibit higher effectiveness and efficiency. Nevertheless, the development of reliable and generalized echo extrapolation algorithm is impeded by three primary challenges: cumulative error spreading, imprecise representation of sparsely distributed echoes, and inaccurate description of non-stationary motion processes. To tackle these challenges, this paper proposes a novel radar echo extrapolation algorithm called Temporal-Spatial Parallel Transformer, referred to as TempEE. TempEE avoids using auto-regression and instead employs a one-step forward strategy to prevent cumulative error spreading during the extrapolation process. Additionally, we propose the incorporation of a Multi-level Temporal-Spatial Attention mechanism to improve the algorithm's capability of capturing both global and local information while emphasizing task-related regions, including sparse echo representations, in an efficient manner. Furthermore, the algorithm extracts spatio-temporal representations from continuous echo images using a parallel encoder to model the non-stationary motion process for echo extrapolation. The superiority of our TempEE has been demonstrated in the context of the classic radar echo extrapolation task, utilizing a real-world dataset. Extensive experiments have further validated the efficacy and indispensability of various components within TempEE.  ( 3 min )

In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest. However, a necessary condition is the unambiguous knowledge of whether each individual is censored or not, which may be incomplete in practice. We therefore propose a study of the Beran estimator when the censoring indicators are generic random variables and discuss necessary conditions for the efficiency of the Beran estimator. From this, we provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.  ( 2 min )

Recently sequential recommendations and next-item prediction task has become increasingly popular in the field of recommender systems. Currently, two state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec. Over the past few years, there have been quite a few publications comparing these two algorithms and proposing new state-of-the-art models. In most of the publications, BERT4Rec achieves better performance than SASRec. But BERT4Rec uses cross-entropy over softmax for all items, while SASRec uses negative sampling and calculates binary cross-entropy loss for one positive and one negative item. In our work, we show that if both models are trained with the same loss, which is used by BERT4Rec, then SASRec will significantly outperform BERT4Rec both in terms of quality and training speed. In addition, we show that SASRec could be effectively trained with negative sampling and still outperform BERT4Rec, but the number of negative examples should be much larger than one.  ( 2 min )

Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions of input data sizes. Most of the analysis tools for logic programs (and other languages) are based on setting up recurrence relations representing (bounds on) the computational cost of predicates, and solving them to find closed-form functions that are equivalent to (or a bound on) them. Such recurrence solving is a bottleneck in current tools: many of the recurrences that arise during the analysis cannot be solved with current solvers, such as Computer Algebra Systems (CASs), so that specific methods for different classes of recurrences need to be developed. We address such a challenge by developing a novel, general approach for solving arbitrary, constrained recurrence relations, that uses machine-learning sparse regression techniques to guess a candidate closed-form function, and a combination of an SMT-solver and a CAS to check whether such function is actually a solution of the recurrence. We have implemented a prototype and evaluated it with recurrences generated by a cost analysis system (the one in CiaoPP). The experimental results are quite promising, showing that our approach can find closed-form solutions, in a reasonable time, for classes of recurrences that cannot be solved by such a system, nor by current CASs.  ( 3 min )

The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning. In this work, we tackle this challenge in the trusted aggregator model, and propose a solution that achieves both objectives simultaneously. We show that employing a quantization scheme based on subtractive dithering at the clients can effectively replicate the normal noise addition process at the aggregator. This implies that we can guarantee the same level of differential privacy against other clients while substantially reducing the amount of communication required, as opposed to transmitting full precision gradients and using central noise addition. We also experimentally demonstrate that the accuracy of our proposed approach matches that of the full precision gradient method.  ( 2 min )

Simplicial complexes prove effective in modeling data with multiway dependencies, such as data defined along the edges of networks or within other higher-order structures. Their spectrum can be decomposed into three interpretable subspaces via the Hodge decomposition, resulting foundational in numerous applications. We leverage this decomposition to develop a contrastive self-supervised learning approach for processing simplicial data and generating embeddings that encapsulate specific spectral information.Specifically, we encode the pertinent data invariances through simplicial neural networks and devise augmentations that yield positive contrastive examples with suitable spectral properties for downstream tasks. Additionally, we reweight the significance of negative examples in the contrastive loss, considering the similarity of their Hodge components to the anchor. By encouraging a stronger separation among less similar instances, we obtain an embedding space that reflects the spectral properties of the data. The numerical results on two standard edge flow classification tasks show a superior performance even when compared to supervised learning techniques. Our findings underscore the importance of adopting a spectral perspective for contrastive learning with higher-order data.  ( 2 min )

Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a least squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset, which consists of three distinct iris plant species. Based on this, we explore the practicality and effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, by strategically exploiting both classical and quantum computing for various subroutines of our algorithm, we effectively mitigate practical challenges associated with the implementation. These include significant improvement in the trainability of the variational ansatz and notable reductions in run-time for cost calculations. Based on the numerical experiments, our approach exhibits the capability of identifying a separating hyperplane in an 8-dimensional feature space. Moreover, it consistently demonstrated strong performance across various instances with the same dataset.  ( 2 min )

Historically, much of machine learning research has focused on the performance of the algorithm alone, but recently more attention has been focused on optimizing joint human-algorithm performance. Here, we analyze a specific type of human-algorithm collaboration where the algorithm has access to a set of $n$ items, and presents a subset of size $k$ to the human, who selects a final item from among those $k$. This scenario could model content recommendation, route planning, or any type of labeling task. Because both the human and algorithm have imperfect, noisy information about the true ordering of items, the key question is: which value of $k$ maximizes the probability that the best item will be ultimately selected? For $k=1$, performance is optimized by the algorithm acting alone, and for $k=n$ it is optimized by the human acting alone. Surprisingly, we show that for multiple of noise models, it is optimal to set $k \in [2, n-1]$ - that is, there are strict benefits to collaborating, even when the human and algorithm have equal accuracy separately. We demonstrate this theoretically for the Mallows model and experimentally for the Random Utilities models of noisy permutations. However, we show this pattern is reversed when the human is anchored on the algorithm's presented ordering - the joint system always has strictly worse performance. We extend these results to the case where the human and algorithm differ in their accuracy levels, showing that there always exist regimes where a more accurate agent would strictly benefit from collaborating with a less accurate one, but these regimes are asymmetric between the human and the algorithm's accuracy.  ( 3 min )

Ball bearings find widespread use in various manufacturing and mechanical domains, and methods based on machine learning have been widely adopted in the field to monitor wear and spot defects before they lead to failures. Few studies, however, have addressed the problem of censored data, in which failure is not observed. In this paper, we propose a novel approach to predict the time to failure in ball bearings using survival analysis. First, we analyze bearing data in the frequency domain and annotate when a bearing fails by comparing the Kullback-Leibler divergence and the standard deviation between its break-in frequency bins and its break-out frequency bins. Second, we train several survival models to estimate the time to failure based on the annotated data and covariates extracted from the time domain, such as skewness, kurtosis and entropy. The models give a probabilistic prediction of risk over time and allow us to compare the survival function between groups of bearings. We demonstrate our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a 0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates further work on incorporating censored data in models for predictive maintenance.  ( 2 min )

The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance.  ( 2 min )

Markov processes are widely used mathematical models for describing dynamic systems in various fields. However, accurately simulating large-scale systems at long time scales is computationally expensive due to the short time steps required for accurate integration. In this paper, we introduce an inference process that maps complex systems into a simplified representational space and models large jumps in time. To achieve this, we propose Time-lagged Information Bottleneck (T-IB), a principled objective rooted in information theory, which aims to capture relevant temporal features while discarding high-frequency information to simplify the simulation task and minimize the inference error. Our experiments demonstrate that T-IB learns information-optimal representations for accurately modeling the statistical properties and dynamics of the original process at a selected time lag, outperforming existing time-lagged dimensionality reduction methods.  ( 2 min )

We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-object interactions, a comprehensive physics-based approach remains a challenge. Existing methods often focus on isolated segments of the interaction process and rely on data-driven techniques that may result in artifacts. In contrast, our proposed method embraces reinforcement learning (RL) and physics simulation to mitigate the limitations of data-driven approaches. Through a hierarchical framework, we first learn skill priors for both body and hand movements in a decoupled setting. The generic skill priors learn to decode a latent skill embedding into the motion of the underlying part. A high-level policy then controls hand-object interactions in these pretrained latent spaces, guided by task objectives of grasping and 3D target trajectory following. It is trained using a novel reward function that combines an adversarial style term with a task reward, encouraging natural motions while fulfilling the task incentives. Our method successfully accomplishes the complete interaction task, from approaching an object to grasping and subsequent manipulation. We compare our approach against kinematics-based baselines and show that it leads to more physically plausible motions.  ( 2 min )

The increasing popularity of large language models (LLMs) has paved the way for their application in diverse domains. This paper proposes a benchmarking framework tailored specifically for evaluating LLM performance in the context of Verilog code generation for hardware design and verification. We present a comprehensive evaluation dataset consisting of 156 problems from the Verilog instructional website HDLBits. The evaluation set consists of a diverse set of Verilog code generation tasks, ranging from simple combinational circuits to complex finite state machines. The Verilog code completions can be automatically tested for functional correctness by comparing the transient simulation outputs of the generated design with a golden solution. We also demonstrate that the Verilog code generation capability of pretrained language models could be improved with supervised fine-tuning by bootstrapping with LLM generated synthetic problem-code pairs.  ( 2 min )

Wide use and availability of the machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new application appears also in biology and agriculture, where we could speak about the detection of infections, parasites and weeds, but also about automated monitoring and early warning systems. This is also connected with the introduction of the easily accessible hardware and development kits such as Arduino, or RaspberryPi family. In this paper, we survey 50 existing papers focusing on the methods of automated beehive monitoring methods using the computer vision techniques, particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Later, we also include analysis of the research trends in this application field and we outline the possible direction of the new explorations. Our paper is aimed also at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce them to its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring.  ( 3 min )

We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound of the policy value. It can be shown that our estimator contains the recently proposed sharp estimator by Dorn and Guo (2022) as a special case, and our method enables a novel extension of the classical marginal sensitivity model using f-divergence. To construct our estimator, we leverage the kernel method to obtain a tractable approximation to the conditional moment constraints, which traditional non-sharp estimators failed to take into account. In the theoretical analysis, we provide a condition for the choice of the kernel which guarantees no specification error that biases the lower bound estimation. Furthermore, we provide consistency guarantees of policy evaluation and learning. In the experiments with synthetic and real-world data, we demonstrate the effectiveness of the proposed method.  ( 2 min )

We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar `Large Observation Models.'  ( 2 min )

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.  ( 2 min )

While real-world applications of reinforcement learning are becoming popular, the security and robustness of RL systems are worthy of more attention and exploration. In particular, recent works have revealed that, in a multi-agent RL environment, backdoor trigger actions can be injected into a victim agent (a.k.a. Trojan agent), which can result in a catastrophic failure as soon as it sees the backdoor trigger action. To ensure the security of RL agents against malicious backdoors, in this work, we propose the problem of Backdoor Detection in a multi-agent competitive reinforcement learning system, with the objective of detecting Trojan agents as well as the corresponding potential trigger actions, and further trying to mitigate their Trojan behavior. In order to solve this problem, we propose PolicyCleanse that is based on the property that the activated Trojan agents accumulated rewards degrade noticeably after several timesteps. Along with PolicyCleanse, we also design a machine unlearning-based approach that can effectively mitigate the detected backdoor. Extensive experiments demonstrate that the proposed methods can accurately detect Trojan agents, and outperform existing backdoor mitigation baseline approaches by at least 3% in winning rate across various types of agents and environments.  ( 3 min )

Federated learning algorithms perform reasonably well on independent and identically distributed (IID) data. They, on the other hand, suffer greatly from heterogeneous environments, i.e., Non-IID data. Despite the fact that many research projects have been done to address this issue, recent findings indicate that they are still sub-optimal when compared to training on IID data. In this work, we carefully analyze the existing methods in heterogeneous environments. Interestingly, we find that regularizing the classifier's outputs is quite effective in preventing performance degradation on Non-IID data. Motivated by this, we propose Learning from Drift (LfD), a novel method for effectively training the model in heterogeneous settings. Our scheme encapsulates two key components: drift estimation and drift regularization. Specifically, LfD first estimates how different the local model is from the global model (i.e., drift). The local model is then regularized such that it does not fall in the direction of the estimated drift. In the experiment, we evaluate each method through the lens of the five aspects of federated learning, i.e., Generalization, Heterogeneity, Scalability, Forgetting, and Efficiency. Comprehensive evaluation results clearly support the superiority of LfD in federated learning with Non-IID data.  ( 2 min )

We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures. Compared to these previous architectures, our proposed model retains the benefits of using an utterance-level bottleneck, while keeping significant representation power and a discretized latent space small enough for efficient prediction from text. We train the model on recordings in the expressive task-oriented dialogues domain and show that SVQ-VAE achieves a statistically significant improvement in naturalness over the VAE and VQ-VAE models. Furthermore, we demonstrate that the SVQ-VAE latent acoustic space is predictable from text, reducing the gap between the standard constant vector synthesis and vocoded recordings by 32%.  ( 2 min )

Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each persons most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings.  ( 2 min )

Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. The framework addresses the challenges above: it seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders. The proposed architecture consists of three layers of agents, each pursuing different objectives. The first layer, comprised of prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon impact while balancing the consumption and production of both renewable and conventional energy. This framework also takes into account fluctuations in energy demand and supply.  ( 2 min )

A growing body of research has focused on the Reinforcement Learning (RL) methods which allow the agent to learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL becomes a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others can train high-quality agents without interacting with the environments. This paradigm has demonstrated effectiveness in critical tasks like robot control, autonomous driving, etc. However, less attention is paid to investigating the security threats to the offline RL system. This paper focuses on backdoor attacks, where some perturbations are added to the data (observations) such that given normal observations, the agent takes high-rewards actions, and low-reward actions on observations injected with triggers. In this paper, we propose Baffle (Backdoor Attack for Offline Reinforcement Learning), an approach that automatically implants backdoors to RL agents by poisoning the offline RL dataset, and evaluate how different offline RL algorithms react to this attack. Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack. Baffle modifies $10\%$ of the datasets for four tasks. Agents trained on the poisoned datasets perform well in normal settings. However, when triggers are presented, the agents' performance decreases drastically by $63.2\%$, $53.9\%$, $64.7\%$, and $47.4\%$ in the four tasks on average. The backdoor still persists after fine-tuning poisoned agents on clean datasets. We further show that the inserted backdoor is also hard to be detected by a popular defensive method. This paper calls attention to developing more effective protection for the open-source offline RL dataset.  ( 3 min )

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets.  ( 2 min )

In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM.  ( 2 min )

In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introduce an approach based on the ideas of importance sampling to obtain an unbiased estimate of the target error. By taking into account difference between desirable error and available data, our method reweights errors at each sample point and neutralizes the shift. Importance sampling technique and kernel density estimation were used for reweighteing. We validate the effectiveness of our approach using artificial data that resemble real-world spatial datasets. Our findings demonstrate advantages of the proposed approach for the estimation of the target error, offering a solution to a distribution shift problem. Overall error of predictions dropped from 7% to just 2% and it gets smaller for larger samples.  ( 2 min )

In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks without human intervention, such as monitoring, spraying, and harvesting. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Generalizing to new crops and environmental conditions is critical for practical applications, as labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from an ensemble of models individually trained on source domains to a student model that can adapt to unseen target domains. To evaluate the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate shapes and covering different terrain styles, weather conditions, and light scenarios for more than 50,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of precision agriculture applications.  ( 2 min )

We study the classical problem of approximating a non-decreasing function $f: \mathcal{X} \to \mathcal{Y}$ in $L^p(\mu)$ norm by sequentially querying its values, for known compact real intervals $\mathcal{X}$, $\mathcal{Y}$ and a known probability measure $\mu$ on $\cX$. For any function~$f$ we characterize the minimum number of evaluations of $f$ that algorithms need to guarantee an approximation $\hat{f}$ with an $L^p(\mu)$ error below $\epsilon$ after stopping. Unlike worst-case results that hold uniformly over all $f$, our complexity measure is dependent on each specific function $f$. To address this problem, we introduce GreedyBox, a generalization of an algorithm originally proposed by Novak (1992) for numerical integration. We prove that GreedyBox achieves an optimal sample complexity for any function $f$, up to logarithmic factors. Additionally, we uncover results regarding piecewise-smooth functions. Perhaps as expected, the $L^p(\mu)$ error of GreedyBox decreases much faster for piecewise-$C^2$ functions than predicted by the algorithm (without any knowledge on the smoothness of $f$). A simple modification even achieves optimal minimax approximation rates for such functions, which we compute explicitly. In particular, our findings highlight multiple performance gaps between adaptive and non-adaptive algorithms, smooth and piecewise-smooth functions, as well as monotone or non-monotone functions. Finally, we provide numerical experiments to support our theoretical results.  ( 2 min )

In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. These critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrastive learning to hone in on critical steps. This algorithm, which we call contrastive introspection (ConSpec), can be added to any existing RL algorithm. ConSpec learns a set of prototypes for the critical steps in a task by a novel contrastive loss and delivers an intrinsic reward when the current state matches one of these prototypes. The prototypes in ConSpec provide two key benefits for credit assignment: (1) They enable rapid identification of all the critical steps. (2) They do so in a readily interpretable manner, enabling out-of-distribution generalization when sensory features are altered. Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon than it is to prospectively predict reward at every step taken in the environment. Altogether, ConSpec improves learning in a diverse set of RL tasks, including both those with explicit, discrete critical steps and those with complex, continuous critical steps.  ( 3 min )

Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp( \mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2 \times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and $A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of $\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n \times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in $\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of matrix multiplication.  ( 3 min )

Hurricanes present major challenges in the U.S. due to their devastating impacts. Mitigating these risks is important, and the insurance industry is central in this effort, using intricate statistical models for risk assessment. However, these models often neglect key temporal and spatial hurricane patterns and are limited by data scarcity. This study introduces a refined approach combining the ARIMA model and K-MEANS to better capture hurricane trends, and an Autoencoder for enhanced hurricane simulations. Our experiments show that this hybrid methodology effectively simulate historical hurricane behaviors while providing detailed projections of potential future trajectories and intensities. Moreover, by leveraging a comprehensive yet selective dataset, our simulations enrich the current understanding of hurricane patterns and offer actionable insights for risk management strategies.  ( 2 min )

Deep neural networks have shown many fruitful applications in this decade. A network can get the generalized function through training with a finite dataset. The degree of generalization is a realization of the proximity scale in the data space. Specifically, the scale is not clear if the dataset is complicated. Here we consider a network for the distribution estimation of the dataset. We show the estimation is unstable and the instability depends on the data density and training duration. We derive the kernel-balanced equation, which gives a short phenomenological description of the solution. The equation tells us the reason for the instability and the mechanism of the scale. The network outputs a local average of the dataset as a prediction and the scale of averaging is determined along the equation. The scale gradually decreases along training and finally results in instability in our case.  ( 2 min )

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained language models, but no open-sourced library is specifically designed for KGs with PLMs at present. In this paper, we present LambdaKG, a library for KGE that equips with many pre-trained language models (e.g., BERT, BART, T5, GPT-3), and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). LambdaKG is publicly open-sourced at https://github.com/zjunlp/PromptKG/tree/main/lambdaKG, with a demo video at this http URL and long-term maintenance.  ( 2 min )

We consider the problem of learning an inner approximation of the region of attraction (ROA) of an asymptotically stable equilibrium point without an explicit model of the dynamics. Rather than leveraging approximate models with bounded uncertainty to find a (robust) invariant set contained in the ROA, we propose to learn sets that satisfy a more relaxed notion of containment known as recurrence. We define a set to be $\tau$-recurrent (resp. $k$-recurrent) if every trajectory that starts within the set, returns to it after at most $\tau$ seconds (resp. $k$ steps). We show that under mild assumptions a $\tau$-recurrent set containing a stable equilibrium must be a subset of its ROA. We then leverage this property to develop algorithms that compute inner approximations of the ROA using counter-examples of recurrence that are obtained by sampling finite-length trajectories. Our algorithms process samples sequentially, which allow them to continue being executed even after an initial offline training stage. We further provide an upper bound on the number of counter-examples used by the algorithm, and almost sure convergence guarantees.  ( 2 min )

Recent advances in learning reusable motion priors have demonstrated their effectiveness in generating naturalistic behaviors. In this paper, we propose a new learning framework in this paradigm for controlling physics-based characters with significantly improved motion quality and diversity over existing state-of-the-art methods. The proposed method uses reinforcement learning (RL) to initially track and imitate life-like movements from unstructured motion clips using the discrete information bottleneck, as adopted in the Vector Quantized Variational AutoEncoder (VQ-VAE). This structure compresses the most relevant information from the motion clips into a compact yet informative latent space, i.e., a discrete space over vector quantized codes. By sampling codes in the space from a trained categorical prior distribution, high-quality life-like behaviors can be generated, similar to the usage of VQ-VAE in computer vision. Although this prior distribution can be trained with the supervision of the encoder's output, it follows the original motion clip distribution in the dataset and could lead to imbalanced behaviors in our setting. To address the issue, we further propose a technique named prior shifting to adjust the prior distribution using curiosity-driven RL. The outcome distribution is demonstrated to offer sufficient behavioral diversity and significantly facilitates upper-level policy learning for downstream tasks. We conduct comprehensive experiments using humanoid characters on two challenging downstream tasks, sword-shield striking and two-player boxing game. Our results demonstrate that the proposed framework is capable of controlling the character to perform considerably high-quality movements in terms of behavioral strategies, diversity, and realism. Videos, codes, and data are available at https://tencent-roboticsx.github.io/NCP/.  ( 3 min )

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the query of the user. In this work, we empirically investigate a few aspects of this domain. Firstly, in addition to traditional features like TF-IDF, BM25 etc., we introduce a BERT-based feature that captures the semantic similarity between the question and answer. Secondly, most of the existing research works have focused on features extracted only from the question part; features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Thirdly, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance. We also analyze importance of the features we use in our investigation. This work is expected to guide the practitioners to select a better set of features for the CQA retrieval task.  ( 2 min )

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a primary radar intelligently targets ice storms based on information collected by a lookahead radiometer. Critical to the intelligent targeting is accurate identification of storm/cloud types from eight bands of radiance collected by the radiometer. The cloud types of interest are: clear sky, thin cirrus, cirrus, rainy anvil, and convection core. We describe multi-step use of Machine Learning and Digital Twin of the Earth's atmosphere to derive such a classifier. First, a digital twin of Earth's atmosphere called a Weather Research Forecast (WRF) is used generate simulated lookahead radiometer data as well as deeper "science" hidden variables. The datasets simulate a tropical region over the Caribbean and a non-tropical region over the Atlantic coast of the United States. A K-means clustering over the scientific hidden variables was utilized by human experts to generate an automatic labelling of the data - mapping each physical data point to cloud types by scientists informed by mean/centroids of hidden variables of the clusters. Next, classifiers were trained with the inputs of the simulated radiometer data and its corresponding label. The classifiers of a random decision forest (RDF), support vector machine (SVM), Gaussian na\"ive bayes, feed forward artificial neural network (ANN), and a convolutional neural network (CNN) were trained. Over the tropical dataset, the best performing classifier was able to identify non-storm and storm clouds with over 80% accuracy in each class for a held-out test set. Over the non-tropical dataset, the best performing classifier was able to classify non-storm clouds with over 90% accuracy and storm clouds with over 40% accuracy. Additionally both sets of classifiers were shown to be resilient to instrument noise.  ( 3 min )

Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning. However, the existing Hierarchical Reinforcement Learning (HRL) approaches relying on symbolic reasoning are often limited as they require a manual goal representation. The challenge in autonomously discovering a symbolic goal representation is that it must preserve critical information, such as the environment dynamics. In this work, we propose a developmental mechanism for subgoal discovery via an emergent representation that abstracts (i.e., groups together) sets of environment states that have similar roles in the task. We create a HRL algorithm that gradually learns this representation along with the policies and evaluate it on navigation tasks to show the learned representation is interpretable and results in data efficiency.  ( 2 min )

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.html  ( 2 min )

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual Market Dataset with market dynamics, stock ticker, and history state as context, leveraging a market dynamics modeling method that combines linear regression and Dynamic Time Warping clustering to extract market dynamics; ii) we present Market-GAN, a novel architecture incorporating a Generative Adversarial Networks (GAN) for the controllable generation with context, an autoencoder for learning low-dimension features, and supervisors for knowledge transfer; iii) we introduce a two-stage training scheme to ensure that Market-GAN captures the intrinsic market distribution with multiple objectives. In the pertaining stage, with the use of the autoencoder and supervisors, we prepare the generator with a better initialization for the adversarial training stage. We propose a set of holistic evaluation metrics that consider alignment, fidelity, data usability on downstream tasks, and market facts. We evaluate Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and showcase superior performance in comparison to 4 state-of-the-art time-series generative models.  ( 3 min )

Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer normalization confines the latent features to a hyper-sphere, subsequently enabling attention to mold the semantic representation of words on this surface. This geometric viewpoint seamlessly connects established properties such as iterative refinement and contextual embeddings. We validate our insights by probing a pre-trained 124M parameter GPT-2 model. Our findings reveal clear query-key attention patterns in early layers and build upon prior observations regarding the subject-specific nature of attention heads at deeper layers. Harnessing these geometric insights, we present an intuitive understanding of transformers, depicting them as processes that model the trajectory of word particles along the hyper-sphere.  ( 2 min )

In this work, we provide a characterization of the feature-learning process in two-layer ReLU networks trained by gradient descent on the logistic loss following random initialization. We consider data with binary labels that are generated by an XOR-like function of the input features. We permit a constant fraction of the training labels to be corrupted by an adversary. We show that, although linear classifiers are no better than random guessing for the distribution we consider, two-layer ReLU networks trained by gradient descent achieve generalization error close to the label noise rate. We develop a novel proof technique that shows that at initialization, the vast majority of neurons function as random features that are only weakly correlated with useful features, and the gradient descent dynamics 'amplify' these weak, random features to strong, useful features.

Score-based methods for learning Bayesain networks(BN) aim to maximizing the global score functions. However, if local variables have direct and indirect dependence simultaneously, the global optimization on score functions misses edges between variables with indirect dependent relationship, of which scores are smaller than those with direct dependent relationship. In this paper, we present an identifiability condition based on a determined subset of parents to identify the underlying DAG. By the identifiability condition, we develop a two-phase algorithm namely optimal-tuning (OT) algorithm to locally amend the global optimization. In the optimal phase, an optimization problem based on first-order Hilbert-Schmidt independence criterion (HSIC) gives an estimated skeleton as the initial determined parents subset. In the tuning phase, the skeleton is locally tuned by deletion, addition and DAG-formalization strategies using the theoretically proved incremental properties of high-order HSIC. Numerical experiments for different synthetic datasets and real-world datasets show that the OT algorithm outperforms existing methods. Especially in Sigmoid Mix model with the size of the graph being ${\rm\bf d=40}$, the structure intervention distance (SID) of the OT algorithm is 329.7 smaller than the one obtained by CAM, which indicates that the graph estimated by the OT algorithm misses fewer edges compared with CAM.Source code of the OT algorithm is available at https://github.com/YafeiannWang/optimal-tune-algorithm.

The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations. In particular, the simplicial complex has inspired generalizations of graph neural networks (GNNs) to simplicial complex-based models. Learning on such systems requires large amounts of data, which can be expensive or impossible to obtain. We propose data augmentation of simplicial complexes through both linear and nonlinear mixup mechanisms that return mixtures of existing labeled samples. In addition to traditional pairwise mixup, we present a convex clustering mixup approach for a data-driven relationship among several simplicial complexes. We theoretically demonstrate that the resultant synthetic simplicial complexes interpolate among existing data with respect to homomorphism densities. Our method is demonstrated on both synthetic and real-world datasets for simplicial complex classification.

In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first considers adapting multiple heterogeneous labeled source domains to an unlabeled target domain. On the other hand, the second attacks the problem of synthesizing a small summary containing all the information about the datasets. We thus consider a new problem called MSDA-DD. To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching. We thoroughly experiment with this novel problem on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous Stirred Tank Reactor, and Case Western Reserve University), where we show that, even with as little as 1 sample per class, one achieves state-of-the-art adaptation performance.

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets.

In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior, which is known as posterior collapse and hinders the quality of representation learning. To mitigate this problem, an adjustable hyperparameter beta has been introduced in the VAE. This paper presents a closed-form expression to assess the relationship between the beta in VAE, the dataset size, the posterior collapse, and the rate-distortion curve by analyzing a minimal VAE in a high-dimensional limit. These results clarify that a long plateau in the generalization error emerges with a relatively larger beta. As the beta increases, the length of the plateau extends and then becomes infinite beyond a certain beta threshold. This implies that the choice of beta, unlike the usual regularization parameters, can induce posterior collapse regardless of the dataset size. Thus, beta is a risky parameter that requires careful tuning. Furthermore, considering the dataset-size dependence on the rate-distortion curve, a relatively large dataset is required to obtain a rate-distortion curve with high rates. Extensive numerical experiments support our analysis.

Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is generally described via an ensemble or a distribution. Each member of the ensemble is usually mapped to a random number seed (explicitly or implicitly). With the goal of finding not only the input parameter settings but also the random seeds that are consistent with the ground truth, we propose a class of Gaussian process (GP) surrogates along with an optimization strategy based on Thompson sampling. This Trajectory Oriented Optimization (TOO) approach produces actual trajectories close to the empirical observations instead of a set of parameter settings where only the mean simulation behavior matches with the ground truth.

We define a family of $C^1$ functions which we call "nowhere coexpanding functions" that is closed under composition and includes all $C^3$ functions with non-positive Schwarzian derivative. We establish results on the number and nature of the fixed points of these functions, including a generalisation of a classic result of Singer.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It first leverages multivariate responses to separate marginal and uncorrelated confounding effects, recovering the confounding coefficients' column space. Subsequently, latent factors and primary effects are jointly estimated, utilizing $\ell_1$-regularization for sparsity while imposing orthogonality onto confounding coefficients. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish various effects' identification conditions and non-asymptotic error bounds. We show effective Type-I error control of asymptotic $z$-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.

We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits. In particular, we utilize the Okounkov-Vershik approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the equivalence between $\operatorname{SU}(d)$ and $S_n$ irrep bases and to establish the $S_n$-equivariant Convolutional Quantum Alternating Ans\"atze ($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA is able to generate any unitary in any given $S_n$ irrep sector, which may serve as a universal model for a wide array of quantum machine learning problems with the presence of SU($d$) symmetry. Our method provides another way to prove the universality of Quantum Approximate Optimization Algorithm (QAOA) and verifies that 4-local SU($d$) symmetric unitaries are sufficient to build generic SU($d$) symmetric quantum circuits up to relative phase factors. We present numerical simulations to showcase the effectiveness of the ans\"atze to find the ground state energy of the $J_1$--$J_2$ antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our work provides the first application of the celebrated Okounkov-Vershik's $S_n$ representation theory to quantum physics and machine learning, from which to propose quantum variational ans\"atze that strongly suggests to be classically intractable tailored towards a specific optimization problem.

Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inductive conformal prediction (ICP). This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data. The efficacy of the method is validated across three classification tasks within distinct modalities: filtering drug-induced-liver-injury (DILI) literature with title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced through label permutation. Results show significant enhancements in classification performance: accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% and 89.0%). Our method offers the potential to substantially boost classification performance in multi-modal biomedical machine learning tasks. Importantly, it accomplishes this without necessitating an excessive volume of meticulously curated training data.  ( 3 min )

In many randomized experiments, the treatment effect of the long-term metric (i.e. the primary outcome of interest) is often difficult or infeasible to measure. Such long-term metrics are often slow to react to changes and sufficiently noisy they are challenging to faithfully estimate in short-horizon experiments. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric -- so they can be used to effectively guide decision-making in the near-term. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments. Our procedure first reduces the construction of an optimal proxy metric in a given experiment to a portfolio optimization problem which depends on the true latent treatment effects and noise level of experiment under consideration. We then denoise the observed treatment effects of the long-term metric and a set of proxies in a historical corpus of randomized experiments to extract estimates of the latent treatment effects for use in the optimization problem. One key insight derived from our approach is that the optimal proxy metric for a given experiment is not apriori fixed; rather it should depend on the sample size (or effective noise level) of the randomized experiment for which it is deployed. To instantiate and evaluate our framework, we employ our methodology in a large corpus of randomized experiments from an industrial recommendation system and construct proxy metrics that perform favorably relative to several baselines.  ( 2 min )

In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations. This gives rise to the joint problem of community detection and synchronization. We propose a series of semidefinite relaxations, and prove their exact recovery when extending the celebrated stochastic block model to this new setting where both rotations and cluster identities are to be determined. Numerical experiments demonstrate the efficacy of our proposed algorithms and confirm our theoretical result which indicates a sharp phase transition for exact recovery.  ( 2 min )

We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them.  ( 2 min )

Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp( \mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2 \times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and $A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of $\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n \times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in $\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of matrix multiplication.  ( 3 min )

We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound of the policy value. It can be shown that our estimator contains the recently proposed sharp estimator by Dorn and Guo (2022) as a special case, and our method enables a novel extension of the classical marginal sensitivity model using f-divergence. To construct our estimator, we leverage the kernel method to obtain a tractable approximation to the conditional moment constraints, which traditional non-sharp estimators failed to take into account. In the theoretical analysis, we provide a condition for the choice of the kernel which guarantees no specification error that biases the lower bound estimation. Furthermore, we provide consistency guarantees of policy evaluation and learning. In the experiments with synthetic and real-world data, we demonstrate the effectiveness of the proposed method.  ( 2 min )

Variational algorithms require architectures that naturally constrain the optimisation space to run efficiently. In geometric quantum machine learning, one achieves this by encoding group structure into parameterised quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge. In this paper, we propose the use of spin networks, a form of directed tensor network invariant under a group transformation, to devise SU(2) equivariant quantum circuit ans\"atze -- circuits possessing spin rotation symmetry. By changing to the basis that block diagonalises SU(2) group action, these networks provide a natural building block for constructing parameterised equivariant quantum circuits. We prove that our construction is mathematically equivalent to other known constructions, such as those based on twirling and generalised permutations, but more direct to implement on quantum hardware. The efficacy of our constructed circuits is tested by solving the ground state problem of SU(2) symmetric Heisenberg models on the one-dimensional triangular lattice and on the Kagome lattice. Our results highlight that our equivariant circuits boost the performance of quantum variational algorithms, indicating broader applicability to other real-world problems.  ( 2 min )

We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF). It is consists of a nonparametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash. DNCF has no parameterization dependent on training data, therefore has a strong generalization and robustness to adversarial image manipulation. During inference, we also encourage the network parameters to be nonnegative and create a bi-convex function on the input and parameters, and this adapts to second-order optimization algorithms with insufficient running time, having 10X acceleration over Deep Image Prior. With these tools, we empirically verify its capability to defend image classification deep networks against adversary attack algorithms in real-time.  ( 2 min )

We consider nonconvex-concave minimax problems, $\min_{\mathbf{x}} \max_{\mathbf{y} \in \mathcal{Y}} f(\mathbf{x}, \mathbf{y})$, where $f$ is nonconvex in $\mathbf{x}$ but concave in $\mathbf{y}$ and $\mathcal{Y}$ is a convex and bounded set. One of the most popular algorithms for solving this problem is the celebrated gradient descent ascent (GDA) algorithm, which has been widely used in machine learning, control theory and economics. Despite the extensive convergence results for the convex-concave setting, GDA with equal stepsize can converge to limit cycles or even diverge in a general setting. In this paper, we present the complexity results on two-time-scale GDA for solving nonconvex-concave minimax problems, showing that the algorithm can find a stationary point of the function $\Phi(\cdot) := \max_{\mathbf{y} \in \mathcal{Y}} f(\cdot, \mathbf{y})$ efficiently. To the best our knowledge, this is the first nonasymptotic analysis for two-time-scale GDA in this setting, shedding light on its superior practical performance in training generative adversarial networks (GANs) and other real applications.  ( 3 min )

We introduce a new debiasing framework for high-dimensional linear regression that bypasses the restrictions on covariate distributions imposed by modern debiasing technology. We study the prevalent setting where the number of features and samples are both large and comparable. In this context, state-of-the-art debiasing technology uses a degrees-of-freedom correction to remove shrinkage bias of regularized estimators and conduct inference. However, this method requires that the observed samples are i.i.d., the covariates follow a mean zero Gaussian distribution, and reliable covariance matrix estimates for observed features are available. This approach struggles when (i) covariates are non-Gaussian with heavy tails or asymmetric distributions, (ii) rows of the design exhibit heterogeneity or dependencies, and (iii) reliable feature covariance estimates are lacking. To address these, we develop a new strategy where the debiasing correction is a rescaled gradient descent step (suitably initialized) with step size determined by the spectrum of the sample covariance matrix. Unlike prior work, we assume that eigenvectors of this matrix are uniform draws from the orthogonal group. We show this assumption remains valid in diverse situations where traditional debiasing fails, including designs with complex row-column dependencies, heavy tails, asymmetric properties, and latent low-rank structures. We establish asymptotic normality of our proposed estimator (centered and scaled) under various convergence notions. Moreover, we develop a consistent estimator for its asymptotic variance. Lastly, we introduce a debiased Principal Component Regression (PCR) technique using our Spectrum-Aware approach. In varied simulations and real data experiments, we observe that our method outperforms degrees-of-freedom debiasing by a margin.  ( 3 min )

In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest. However, a necessary condition is the unambiguous knowledge of whether each individual is censored or not, which may be incomplete in practice. We therefore propose a study of the Beran estimator when the censoring indicators are generic random variables and discuss necessary conditions for the efficiency of the Beran estimator. From this, we provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.  ( 2 min )

We consider the problem of approximating the regression function from noisy vector-valued data by an online learning algorithm using an appropriate reproducing kernel Hilbert space (RKHS) as prior. In an online algorithm, i.i.d. samples become available one by one by a random process and are successively processed to build approximations to the regression function. We are interested in the asymptotic performance of such online approximation algorithms and show that the expected squared error in the RKHS norm can be bounded by $C^2 (m+1)^{-s/(2+s)}$, where $m$ is the current number of processed data, the parameter $0<s\leq 1$ expresses an additional smoothness assumption on the regression function and the constant $C$ depends on the variance of the input noise, the smoothness of the regression function and further parameters of the algorithm.  ( 2 min )

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.  ( 3 min )

Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noisy data, was first observed in neural network models trained with gradient descent. To better understand this empirical observation, we consider the generalization error of two-layer neural networks trained to interpolation by gradient descent on the logistic loss following random initialization. We assume the data comes from well-separated class-conditional log-concave distributions and allow for a constant fraction of the training labels to be corrupted by an adversary. We show that in this setting, neural networks exhibit benign overfitting: they can be driven to zero training error, perfectly fitting any noisy training labels, and simultaneously achieve minimax optimal test error. In contrast to previous work on benign overfitting that require linear or kernel-based predictors, our analysis holds in a setting where both the model and learning dynamics are fundamentally nonlinear.  ( 2 min )

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An empirical benchmark on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.  ( 2 min )

The swift progression of machine learning (ML) has not gone unnoticed in the realm of statistical mechanics. ML techniques have attracted attention by the classical density-functional theory (DFT) community, as they enable discovery of free-energy functionals to determine the equilibrium-density profile of a many-particle system. Within DFT, the external potential accounts for the interaction of the many-particle system with an external field, thus, affecting the density distribution. In this context, we introduce a statistical-learning framework to infer the external potential exerted on a many-particle system. We combine a Bayesian inference approach with the classical DFT apparatus to reconstruct the external potential, yielding a probabilistic description of the external potential functional form with inherent uncertainty quantification. Our framework is exemplified with a grand-canonical one-dimensional particle ensemble with excluded volume interactions in a confined geometry. The required training dataset is generated using a Monte Carlo (MC) simulation where the external potential is applied to the grand-canonical ensemble. The resulting particle coordinates from the MC simulation are fed into the learning framework to uncover the external potential. This eventually allows us to compute the equilibrium density profile of the system by using the tools of DFT. Our approach benchmarks the inferred density against the exact one calculated through the DFT formulation with the true external potential. The proposed Bayesian procedure accurately infers the external potential and the density profile. We also highlight the external-potential uncertainty quantification conditioned on the amount of available simulated data. The seemingly simple case study introduced in this work might serve as a prototype for studying a wide variety of applications, including adsorption and capillarity.  ( 3 min )

Artificial intelligence models and methods commonly lack causal interpretability. Despite the advancements in interpretable machine learning (IML) methods, they frequently assign importance to features which lack causal influence on the outcome variable. Selecting causally relevant features among those identified as relevant by these methods, or even before model training, would offer a solution. Feature selection methods utilizing information theoretical quantities have been successful in identifying statistically relevant features. However, the information theoretical quantities they are based on do not incorporate causality, rendering them unsuitable for such scenarios. To address this challenge, this article proposes information theoretical quantities that incorporate the causal structure of the system, which can be used to evaluate causal importance of features for some given outcome variable. Specifically, we introduce causal versions of entropy and mutual information, termed causal entropy and causal information gain, which are designed to assess how much control a feature provides over the outcome variable. These newly defined quantities capture changes in the entropy of a variable resulting from interventions on other variables. Fundamental results connecting these quantities to the existence of causal effects are derived. The use of causal information gain in feature selection is demonstrated, highlighting its superiority over standard mutual information in revealing which features provide control over a chosen outcome variable. Our investigation paves the way for the development of methods with improved interpretability in domains involving causation.  ( 3 min )

Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification. However, when the functional data is observed over a large grid (say, of length $p$), Gaussian process clustering quickly renders itself infeasible, incurring $O(p^2)$ space complexity and $O(p^3)$ time complexity per iteration; and thus prohibiting it's natural adaptation to large environmental applications. To ensure scalability of Gaussian process clustering in such applications, we propose to embed the popular Vecchia approximation for Gaussian processes at the heart of the clustering task, provide crucial theoretical insights towards algorithmic design, and finally develop a computationally efficient expectation maximization (EM) algorithm. Empirical evidence of the utility of our proposal is provided via simulations and analysis of polar temperature anomaly (\href{https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series}{noaa.gov}) data-sets.  ( 2 min )

In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. In these same domains, there may be heterogeneity both in who responds in taking-up treatment, and heterogeneity in treatment efficacy. While optimal treatment rules can maximize causal outcomes across the population, access parity constraints or other fairness considerations can be relevant in the case of encouragement. For example, in social services, a persistent puzzle is the gap in take-up of beneficial services among those who may benefit from them the most. When in addition the decision-maker has distributional preferences over both access and average outcomes, the optimal decision rule changes. We study causal identification, statistical variance-reduced estimation, and robust estimation of optimal treatment rules, including under potential violations of positivity. We consider fairness constraints such as demographic parity in treatment take-up, and other constraints, via constrained optimization. Our framework can be extended to handle algorithmic recommendations under an often-reasonable covariate-conditional exclusion restriction, using our robustness checks for lack of positivity in the recommendation. We develop a two-stage algorithm for solving over parametrized policy classes under general constraints to obtain variance-sensitive regret bounds. We illustrate the methods in two case studies based on data from randomized encouragement to enroll in insurance and from pretrial supervised release with electronic monitoring.  ( 2 min )

The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance.  ( 2 min )

Google has developed an AI algorithm to refine route suggestions on Google Maps, personalizing it based on user data and behavior, allegedly improving the accuracy on an average by 16-24 percent. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/rp21ylzo7iob1.jpg?width=770&format=pjpg&auto=webp&s=737cf3fd278ade42900e8586d626a7c729522c1a Personalized Route Suggestions through AI The AI model comprises 360 million parameters, using real-time data from Maps users to influence factors including travel time, road conditions, tolls, and personal preferences to suggest routes. This technology is grounded on "inverse reinforcement learning" (IRL), specifically a new IRL algorithm - "Receding Horizon Inverse Planning (RHIP)". The Power of RHIP and AI in Maps Google and Deepmind jointly worked to develop RHIP, using complex stochastic models in immediate vicinity areas, but switching to simpler deterministic methods for distant areas for power conservation. The AI improves route suggestions for both driving and two-wheeled vehicles by learning from Maps users' movements and behaviors over time. Google states that this is the largest application of inverse reinforcement learning for route planning to date. Implementation and User Testing Google has applied the algorithm to Maps data globally, but extensive user testing is needed to confirm if the technique consistently produces better routes. Previous attempts at using AI systems for route planning on a large scale have often failed due to the complexity of road networks. (source) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

There are a number of reasons why some Republicans may be in denial about Trump's role in the January 6th Capitol riot. Trump has been very effective at cultivating a cult of personality among his supporters. These supporters are often fiercely loyal to him and willing to believe whatever he says, even if it contradicts reality. Many Republicans have been reluctant to criticize Trump for fear of alienating his supporters. This is especially true for Republican politicians who are up for re-election in 2022. Some Republicans may genuinely believe that Trump did not incite the riot. They may point to the fact that he did not explicitly call for violence, or that he told his supporters to "peacefully and patriotically make your voices heard." Other Republicans may be motivated by partis…  ( 10 min )

Factory farming is a cruel and unsustainable way to produce meat. Animals are crammed into small, filthy spaces, where they are often denied their basic needs. They are given antibiotics to prevent disease and growth hormones to make them grow faster. This leads to a variety of health problems for the animals, as well as for the people who eat their meat. Clean meat is a new technology that offers a more humane and sustainable way to produce meat. It is made from animal cells that are grown in a lab, without the need for animals to be raised or slaughtered. Clean meat is still in its early stages of development, but it has the potential to revolutionize the meat industry. Here are some of the reasons why factory farming must be replaced by clean meat: It is cruel to animals. Factory f…  ( 10 min )

News provided by aibrews.com Stability AI launched Stable Audio, a generative AI tool for music & sound generation from text. The underlying latent diffusion model architecture uses audio conditioned on text metadata as well as audio file duration and start time [Details]. Coqui released XTTS - a new voice generation model that lets you clone voices in 13 different languages by using just a quick 3-second audio clip [Details]. Microsoft Research released and open-sourced Phi-1.5 - a 1.3 billion parameter transformer-based model with performance on natural language tasks comparable to models 5x larger [Paper ]. Project Gutenberg, Microsoft and MIT have worked together to use neural text-to-speech to create and release thousands of human-quality free and open audiobooks [Details]. Res…  ( 11 min )

Has anyone dropped an opensource tool like run.ai, to leverage multiple gpus / distribute the workload a bit more efficiently? I'm loving some of the single gpu llm modifications that have been dropping recently (have a couple i've tested that ran well on 4090 and 3090ti in the lab), but i've got a plethora of 8 & 12 gig 3xxx series cards i'd love to take advantage of beyond passthroughs to individual vms. Looking for any solutions. Speed isn't as important as the ability to distributively run larger models. submitted by /u/SwallowedBuckyBalls [link] [comments]  ( 9 min )

submitted by /u/glenniszen [link] [comments]  ( 8 min )

I am looking for a free AI tool, site (may be paid with free account limited options) that can do a summary of English article (like BBC, youtube) based on most frequently used English words, for example Oxford list of 3000 most frequently used words in English. In other words if word is not in that list it should replace the word with the one that is on the list or its definition. Pdf submitted by /u/vibrantform [link] [comments]  ( 9 min )

I said to Bard: It seems like these are difficult times. Compare today with the WWII years, explaining how much we have to be grateful for. Bard said: I agree that these are difficult times. The world is facing many challenges, including the COVID-19 pandemic, climate change, and political instability. However, I believe that we have much to be grateful for today compared to the WWII years. Here are just a few things: We live in a time of peace. World War II was a global conflict that caused the deaths of millions of people. Today, there are no major wars raging on. We have access to advanced medical care. During WWII, many people died from diseases that are now easily treatable. Today, we have antibiotics, vaccines, and other medical advances that have saved countless lives. We have a higher standard of living. In the WWII years, many people lived in poverty. Today, most people in the developed world have access to clean water, food, and shelter. We have more opportunities for education and advancement. In the WWII years, many people were denied an education because of their race, gender, or social class. Today, education is more accessible than ever before, and people from all backgrounds have the opportunity to succeed. We have a better understanding of the world. During WWII, there was a lot of misinformation and propaganda. Today, we have access to more information than ever before, and we can make more informed decisions about our lives. Of course, there are still many challenges that we face today. But I believe that we have come a long way since the WWII years, and we have much to be grateful for. Let us never forget the sacrifices that were made during that time, and let us continue to work towards a better future for all. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

The AI-based techno-economic war between the West and China will determine the global distribution of technology, economic benefits, and influence. The winner of this race, particularly in AI, will have the power to accelerate GDP growth and project global economic benefits. China is actively pursuing technological leadership in AI and 5G wireless, while the West must prioritize technological talent influx and leverage its venture and technology ecosystem. Aggressive AI regulation could hinder the West's momentum in this race. The risks of this war and AI technology are significant, but it is a greater risk to let adversaries have unconstrained power. Global treaties are not enforceable solutions to regulate AI. Source : https://medium.com/@vkhosla/what-is-at-stake-in-this-ai-based-techno-economic-war-between-the-west-and-china-8f76bd291be7 submitted by /u/NuseAI [link] [comments]  ( 9 min )

There's no need to specify which product fulfills your needs, but we'd like everyone to focus on describing their current frustrations and the issues they urgently hope AI can help them with. submitted by /u/Minna_Z [link] [comments]  ( 9 min )

submitted by /u/Agitated-Spell3979 [link] [comments]  ( 8 min )

Hello everyone! I'm embarking on a project to create an AI-model hub—a platform where users can upload and utilize their AI models. While I'm aware of popular platforms that offer this, my primary goal is for educational purposes. I'd greatly appreciate any recommendations for helpful articles, videos, or codebases to guide me on this journey. Thanks in advance! submitted by /u/Electronic-Choice-86 [link] [comments]  ( 9 min )

Hello everyone, Introducing LLMa: ChatGPT built around YOU (getllma.com) - a dedicated service offering hands-on expertise to integrate state-of-the-art generative AI tailored for your projects. We utilize open-source models and train them to outperform GPT-4 on tasks specific to your domain. Envision having a seasoned AI specialist on your team, ensuring your model not only rivals the big players but excels in your unique challenges. 🌟 Why LLMa? Personalized Expertise: Our team collaborates closely with you, delving into your needs and sculpting a model that thrives in your domain. Bespoke Training: We refine open-source models (LLaMa, T5, etc.) with plenty of secret tricks to specialize and surpass GPT-4's performance for your specific tasks. Cost-Effective: LLMa tends to be around 100x cheaper than GPT-4, offering significant savings. No recurring fees; invest in a one-time fee based on your model's complexity. Full Ownership: We hand over the model files/weights to you. It's entirely yours, ensuring total privacy with no PII leaks. Deployment Assistance: Beyond just crafting the model, we can guide you in deploying it, ensuring a seamless integration into your operations. Ongoing Support: From initial brainstorming to model deployment, we're with you, ensuring success at every phase. 💼 Tailored for Enterprises: LLMa is meticulously crafted for enterprises that aim for a high-performing, bespoke AI solution. Transparent pricing begins at $500, contingent on your distinct requirements. ❓ Navigating the Generative AI Terrain? Embarking on the vast journey of generative AI? LLMa is your compass. We aid in defining challenges, strategizing solutions, and optimizing the AI potential for your endeavors. If LLMa piques your interest or if you have any queries, fill-in the form, drop a comment below or DM me. I'm all ears and eager to connect! submitted by /u/iliashark [link] [comments]  ( 9 min )

Quick template that bootstraps Amazon SageMaker running a LlaMa 2 model from Hugging Face. Everything deployed as code (Python), no manual tweaking in the SageMaker console. www.pulumi.com/blog/mlops-huggingface-llm-aws-sagemaker-python/ submitted by /u/kao-pulumi [link] [comments]  ( 9 min )

Am trying to train GANs for oversampling a minority text class (am feeding it only the minority class), but the results dont seem to improve much (AUC only improves by .03 so far). while basic oversampling techniques like SMOTE gives way better results. also am using a vector representation for the whole text instead of word embedding(same used for SMOTE), i tried different architectures with CNN. is there any tricks maybe in training the discriminator and generator ? i can't seem to find the problem submitted by /u/SlightSecretaryB [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07870 Github: https://github.com/aiwaves-cn/agents Abstract: Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. https://preview.redd.it/3bdi71r5rgob1.jpg?width=1131&format=pjpg&auto=webp&s=760942c19be6ecda791414c812a77e72751c526d https://preview.redd.it/howf64r5rgob1.jpg?width=1656&format=pjpg&auto=webp&s=636744fccab7a1c2bafb902bad5dbb647440fff5 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hi all, I have about 100K images on my machine and I am looking for a tool that can help me do some QA on it. Example features I would love: Search and visualize all images with a prefix "cls1_" or "cls2_" Easily rename file names if they're not named correctly Visualize all captions associated with each image We can assume they have the same name but with extension ".txt" or ".captions" Or there's a metadata.json linking between "img_file" and "caption_file" Easily edit captions in the dashboard I can also work with some kind of metadata file instead of relying on filename logic if it really helps a certain tool. I prefer a locally run, open-source tool. It would be a problem for me to upload this data to any online platform. Many thanks in advance for any help or guidance. submitted by /u/JYP_Scouter [link] [comments]  ( 9 min )

Before training my model im going through multiple steps to collect and process my data. One of these steps to is calculate values from algorithmic and mathematical functions. In my training and testing data the values are around 12-15 decimal places. I then split the data without shuffling. Training, validation and testing averages at 75% accuracy. Now my next step I wanted to do a "real world" data test where I collect the exact same data as my testing data and predict it using my previously built model but the values inside test_dataset1 are different to test_dataset2 within the last 6-9 decimal places even though the data is going through the exact same code. For example Test_dataset1 Value : 1.123456789 Test_dataset2 Value : 1.123456987 This messes with my prediction and its making me wonder aswell as standrising my data should I be rounding my float values to say 4-6 decimal places? submitted by /u/paddockson [link] [comments]  ( 9 min )

What are the key differences between the Tensor Processing Unit (TPU) found in Google Tensor chips and the Neural Engine found in Apple's A and M series chips? Are they the same things? Or is the TPU only available for Google's own AI, while the Neural Engine is available to all developers for accelerating AI for all apps, if they decide to? Can developers optimize apps for Google Tensor like they can for the Neural Engine? If not, how do developers take advantage of machine learning acceleration chips on Google Pixel or Android in general? If yes, let's say a developer optimizes their app for the Google TPU, will they need to re-optimize for other chips like Samsung's NPU or Qualcomm AI too? If not, how well do they run? Are they the same fast and efficient? submitted by /u/GRguy_21 [link] [comments]  ( 9 min )

Working on Final year project in the field on computer vision: Image Dehazing. I aim at having a novel approch for better dehazing of face haze images. Have read papers related to single image dehazing & face SR/Deblur. Any thoughts/ suggestions/ leads would be appreciated. submitted by /u/GahlotB [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07315 Abstract: Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer normalization confines the latent features to a hyper-sphere, subsequently enabling attention to mold the semantic representation of words on this surface. This geometric viewpoint seamlessly connects established properties such as iterative refinement and contextual embeddings. We validate our insights by probing a pre-trained 124M parameter GPT-2 model. Our findings reveal clear query-key attention patterns in early layers and build upon prior observations regarding the subject-specific nature of attention heads at deeper layers. Harnessing these geometric insights, we present an intuitive understanding of transformers, depicting them as processes that model the trajectory of word particles along the hyper-sphere. ​ https://preview.redd.it/0i302t857gob1.png?width=1864&format=png&auto=webp&s=1da999c014979bdb6c99809d5b38eb5ccfd717d0 submitted by /u/CoolThingsOnTop [link] [comments]  ( 9 min )

Repo -> https://github.com/MichaelisTrofficus/hampel_filter The Python library hampel implements the Hampel Filter, which is generally used to detect anomalies in data with a timeseries structure. It basically consists of a sliding window of a parameterizable size. The library was in plain Python before (using pandas for all the sliding operations, median computation etc), but now it has been replaced by a Cython implementation, which speeds up things quite a bit! 😀 It also provides much more valuable information (thresholds, median absolute deviations, etc.), allowing us to create plots like this one: https://preview.redd.it/6j4ubiwgmfob1.png?width=800&format=png&auto=webp&s=bbc56777fce30a464d0bb33ac5126033b3413838 ​ submitted by /u/Hefty-Consequence443 [link] [comments]  ( 9 min )

Hi everybody. Currently, I am building a Deep Learning model with the task of automatically generating random posts and tweets. The characteristic is that it must have the personality of the writer, for example the user is the CEO of company A, then the generated post must have the writing style of the CEO or company A, similar to other users. Actually, I don't know where to start solving this problem. I intend to use RWKV to do this problem but I'm not sure if it is a good direction or not. Is there any related research or can anyone who has done this problem give me some suggestions? submitted by /u/unknow_from_vietnam [link] [comments]  ( 9 min )

Paper. I am not affiliated with this work or its authors. Abstract: Transformers have become the dominant model in deep learning, but the reason for their superior performance is poorly understood. Here, we hypothesize that the strong performance of Transformers stems from an architectural bias towards mesa-optimization, a learned process running within the forward pass of a model consisting of the following two steps: (i) the construction of an internal learning objective, and (ii) its corresponding solution found through optimization. To test this hypothesis, we reverse-engineer a series of autoregressive Transformers trained on simple sequence modeling tasks, uncovering underlying gradient-based mesa-optimization algorithms driving the generation of predictions. Moreover, we show that the learned forward-pass optimization algorithm can be immediately repurposed to solve supervised few-shot tasks, suggesting that mesa-optimization might underlie the in-context learning capabilities of large language models. Finally, we propose a novel self-attention layer, the mesa-layer, that explicitly and efficiently solves optimization problems specified in context. We find that this layer can lead to improved performance in synthetic and preliminary language modeling experiments, adding weight to our hypothesis that mesa-optimization is an important operation hidden within the weights of trained Transformers. Twitter thread about the paper from one of the paper's authors. Nitter thread, for those who aren't signed into Twitter but want to see the entire Twitter thread. Background info: Mesa-Optimization: Explain it like I'm 10 Edition. submitted by /u/Wiskkey [link] [comments]  ( 9 min )

Relevant Paper: 2307.08621.pdf (arxiv.org) So the definition of the recurrent representation of the retention mechanism is below Sn = γSn−1 + K⊺nVn Retention(Xn) = QnSn, n = 1, · · · , |x| γ is a decay factor, and K, Q, and V have their standard transformer definitions. What confuses me is the derivation of Sn. The formula makes it look like a scalar. But if that's the case, are we saying that for a given token, the retention mechanism is just multiplying the Query by a scalar? That's surprising! How is that able to provide enough context? Here is some code I wrote with GPT to show my understanding of how it works. Is this correct? I use 3 arbitrary tokens of dimension 3, and then a pick arbitrary K Q and V matrices. I also initialize gamma to 0.5 import numpy as np # Tokens x1…  ( 10 min )

Help required from NLP and Text Researchers !! Hello everyone! I have Urdu language transcriptions (text) which contain many misspelled words that are not part of the Urdu language. I wanted to know do we have any good NLP techniques or methods which can solve this problem for Urdu language? I want to replace these misspelled words with the correct words in Urdu. I have already tried Python libraries and methods such as indic-nlp, Levenshtein distance, UrduHack, Word2vec Urdu etc, but they weren't able to solve this problem. Some of the methods require Urdu dictionaries to find the correct word, which I'm also unable to find open-source on internet (please also help in that if possible). Will appreciate everyone's help and response to this. Thank you! submitted by /u/a_r182 [link] [comments]  ( 9 min )

Hi everyone! I am a (soon graduating) MSc student at a top European university and I'd like to apply for a ML PhD in the US this Fall. I've done my research on schools and advisors, but I figured there's no harm in also asking in this subreddit. What are some groups/professors that do ML research at US unis in deep learning theory (specifically foundations) and optimization? As an example, I'm talking about topics such as: https://arxiv.org/abs/1902.08129, https://arxiv.org/abs/1711.04735, https://arxiv.org/abs/2306.04637. Thank you all! Cheers! submitted by /u/AlexIsEpic24 [link] [comments]  ( 9 min )

Help required from NLP and Text Researchers !! Hello everyone! I have Urdu language transcriptions (text) which contain many misspelled words that are not part of the Urdu language. I wanted to know do we have any good NLP techniques or methods which can solve this problem for Urdu language? I want to replace these misspelled words with the correct words in Urdu. I have already tried Python libraries and methods such as indic-nlp, Levenshtein distance, UrduHack, Word2vec Urdu etc, but they weren't able to solve this problem. Some of the methods require Urdu dictionaries to find the correct word, which I'm also unable to find open-source on internet (please also help in that if possible). Will appreciate everyone's help and response to this. Thank you! submitted by /u/a_r182 [link] [comments]  ( 9 min )

So I’m starting my masters thesis project in ML ASAP and need a research topic. What areas (if any) are currently hot / feasible to do research in roughly 6 months with fairly limited compute access? I’m more interested in theory / research heavy areas rather than applied. And probably happier to dig into some hard math rather than taking on a software engineering type project. Any thoughts or general feedback very welcome! Thanks! submitted by /u/Professional-Pace158 [link] [comments]  ( 9 min )

Just came across this property of matrices that I have never seen before as I am contributing to the NumPy codebase and someone asked for this feature to be added and it got me thinking. It is defined as: A skew-symmetric matrix is a square matrix whose transpose equals to its negative. It should satisfy the below condition: AT = –A Just wondering do these have any applications in ML at all? I never came across this in my math classes so just wondering if the property pops up anywhere else in the field. Maybe in 3D space applications? Or maybe RBG data augmentation? But yeah not 100% sure. submitted by /u/Ok_Reality2341 [link] [comments]  ( 9 min )

I’m an electrical engineering student in college currently, and have been learning about ML for a few months now. I will be starting a ML research paper under my professor from next week. However, my DSA skills are quite sub-par. Should I focus more on ML math and projects, or take sufficient time out for leetcode as well? submitted by /u/4R1N1493 [link] [comments]  ( 9 min )

Hi, I have a dataset that consists of 2D trajectories and I am aiming to develop an autoencoder architecture to learn a compressed set of features that reasonable represents and can reconstruct the trajectories. The trajectories may look something like this as an example. A 2D image as input would seem to require a very sparse representation with high resolution to track the trajectory path. I am hoping there is a better way to input the path without requiring high resolution. An alternative might be to use a LSTM structure to input as a sequence, although not sure that solves the resolution issue. Do you have any suggestions? I've worked with 1d time series and 2D images just fine but this is a bit different. ​ ​ https://preview.redd.it/vqz8y3o69bob1.png?width=2020&format=png&auto=webp&s=d8bcc2fe311743c8e78a96055e68f1ad364b48c3 submitted by /u/ZeApelido [link] [comments]  ( 9 min )

If you ask someone a question and they say “yes” immediately, that gives you different information than if they pause and slowly say “yes.” The information you receive is not just the response but also the time it took to generate the response. Encryption can be analogous. The time it takes to encrypt data can […] Timing attacks first appeared on John D. Cook.  ( 5 min )

I concluded the previous post by saying elliptic curve Diffie-Hellman key exchange (ECDHE) requires smaller keys than finite field Diffie-Hellman (FFDHE) to obtain the same level of security. How much smaller are we talking about? According to NIST recommendations, a 256-bit elliptic curve curve provides about the same security as working over a 3072-bit finite […] Elliptic curve Diffie-Hellman key exchange first appeared on John D. Cook.  ( 6 min )

Diffie-Hellman key exchange is conceptually simple. Alice and Bob want to generate a shared cryptographic key. They want to use asymmetric (public) cryptography to share a symmetric (private) key. The starting point is a large prime p and a generator 1 < g < p. Alice generates a large random number x, her private key, […] Finite field Diffie Hellman primes first appeared on John D. Cook.  ( 6 min )

submitted by /u/keghn [link] [comments]  ( 8 min )

So I want to create a neural network from scratch, like no lib(tensorflow, pytorch, etc…), so what’s the best book for that, I know both calculus and statistics, so I’m assuming that the math wouldn’t be a problem. Also I will be using Cuda for its speed. submitted by /u/GateCodeMark [link] [comments]  ( 9 min )

https://news.accelerationrobotics.com/robotperf-beta/ Together with AMD, Intel, Ford, Harvard, Klagenfurt University, Georgia Institute of Technology, Boston University, Johannes Kepler University Linz, Barnard College, Columbia University and Carnegie Mellon University we are thrilled to introduce the beta release of RobotPerf™ Benchmarks, an advanced benchmarking suite crafted specifically to evaluate robotics computing performance using ROS 2 as its baseline. In this beta release, we not only showcase new benchmarks and results but also introduce novel visualization capabilities. The complete release is available at https://github.com/robotperf/benchmarks/releases/tag/beta. https://preview.redd.it/5whys5ufudob1.png?width=1562&format=png&auto=webp&s=08a6e22a0b07b26fa6340f59ec9df822ab49c9d0 submitted by /u/pablocarrera [link] [comments]  ( 9 min )

Hi, i just finished the coursera ml course and i wanted to create myself a double dqn model but my model don't seem to learn anything, it always return very low rewards (-100 to -300) even after playing 2000 episodes. I've been stuck on this for 4 days without any hope to find the solution, any help would be welcome :') thank you in advance import random import numpy as np import gymnasium as gym import tensorflow as tf from collections import deque, namedtuple from tensorflow.keras import Sequential, Input from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.losses import MeanSquaredError import matplotlib.pyplot as plt # function creating the models def createModel(inputSize, outputSize): model = Sequential([ Input(inputSize),…  ( 10 min )

I'm confused with several algorithms that are based on an actor-critic approach. In TD3 and SAC, it is understandable that each of them is implemented to serve their purpose (deterministic and stachastic action). But in Dreamer algorithm (DreamerV3), why does it require to combine actor and critic network to the model-based planning approach, as the model-based also able to perform an action by planning to the simulation state. It is mean that using model-based to simulate the possible future then update the critic according to the simulation might sound good in training an agent? submitted by /u/AnnonymeowCat [link] [comments]  ( 9 min )

Posted by Haolin Jia, Software Engineer, and Qifei Wang, Senior Software Engineer, Core ML In recent years, we have witnessed rising interest across consumers and researchers in integrated augmented reality (AR) experiences using real-time face feature generation and editing functions in mobile applications, including short videos, virtual reality, and gaming. As a result, there is a growing demand for lightweight, yet high-quality face generation and editing models, which are often based on generative adversarial network (GAN) techniques. However, the majority of GAN models suffer from high computational complexity and the need for a large training dataset. In addition, it is also important to employ GAN models responsibly. In this post, we introduce MediaPipe FaceStylizer, an effi…  ( 92 min )

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases, or APIs to accomplish complex tasks. […]  ( 13 min )

With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.  ( 10 min )

Please provide Arkiv links. If you want to share your thoughts then go for it. By new I mean within the last 6 months. submitted by /u/I_will_delete_myself [link] [comments]  ( 9 min )

What are some good papers to understand posterior collapse in VAEs? submitted by /u/randomkolmogorov [link] [comments]  ( 9 min )

Full Video Tutorial: https://www.linkedin.com/posts/moisesdias\_english-version-below-tutorial-crie-activity-7107686497885011969-ZLVW/ Hello everyone! Have you ever thought about how to create an object detection system using YOLO that works with any game? If you're interested, I've created a tutorial with all the steps to develop this system. I'll leave a link to the video where I demonstrate the process step by step using the game Diablo 2 as an example. I hope you enjoy it, and if you have any suggestions, feel free to send a message or comment here! submitted by /u/moisesdepaulodias [link] [comments]  ( 9 min )

Good Evening, I and some fellow students are taking a SE class and are looking for relevant information regarding esrb ratings and games for a research project. Does anyone know of any data pertaining to relevant esrb info that we can access? We have a few sources and are waiting to hear back from esrb themselves. Would anyone know if they permit web scraping or if there is a csv containing relevant information, or even perhaps an api we could use? Any information would help and thank you all for taking the time to read this. Thanks in advance! submitted by /u/GOD_LIKE_WOW [link] [comments]  ( 9 min )

I'm currently working on a project to give a quick summary of long articles/conversations. I'm running llama-2-7b-chat-hf with 4bit quantization on a g5.2xlarge instance on sagemaker. The method I'm using is map_reduce (option 2)from this webpage https://python.langchain.com/docs/use_cases/summarization) Of everything I've tried this is the only one that's been able to do decent summaries in a reasonable amount of time. However with really long articles (10,000+ words) it takes ~6 minutes before giving an output. I tried running this same thing on a g5.12xlarge instance which has 4 A10G gpus but it hasn't reduced the time by any noticeable amount. Is there anything else I could be doing to speed this up? submitted by /u/Able_Body_9654 [link] [comments]  ( 9 min )

so as the question states, i want to use llama2 to generate an answer for the question based on the context (or the article for more precision), no finetuning is needed, just want to predict the answer, but i can't find what's the right prompt i should use to get a well structured answer. my dataset contains two columns, one for articles, and the other column is for the question, Example: context: article talking about world war 2. question : based on the text, describe how the ww2 had started, and what were the political effects on Europe? submitted by /u/kaoutar- [link] [comments]  ( 9 min )

There's a new open-access foundational audio model in town! Standing on the shoulders of TorToiSe TTS - XTTS allows cross-language and multi-lingual speech generation with just 3 lines of code 🐸 Key facts about the model: 1. Supports 13 languages. 2. Voice cloning with just a 3-second audio clip. 3. Emotion and style transfer by cloning. 4. Cross-language voice cloning. Try it out on HF Hub: https://huggingface.co/spaces/coqui/xtts submitted by /u/vaibhavs10 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07062 Abstract: We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code size. The model takes as input unoptimized assembly and outputs a list of compiler options to best optimize the program. Crucially, during training, we ask the model to predict the instruction counts before and after optimization, and the optimized code itself. These auxiliary learning tasks significantly improve the optimization performance of the model and improve the model's depth of understanding. We evaluate on a large suite of test programs. Our approach achieves a 3.0% improvement in reducing instruction counts over the compiler, outperforming two state-of-the-art baselines that require thousands of compilations. Furthermore, the model shows surprisingly strong code reasoning abilities, generating compilable code 91% of the time and perfectly emulating the output of the compiler 70% of the time. https://preview.redd.it/f9c7kh7bd9ob1.jpg?width=1530&format=pjpg&auto=webp&s=287fffa714936da9b9a5141b7e01609942416156 https://preview.redd.it/z4a0ce7bd9ob1.jpg?width=1537&format=pjpg&auto=webp&s=e6275b2b53fa6f431b87940784629b3270c656f9 https://preview.redd.it/89toie7bd9ob1.jpg?width=750&format=pjpg&auto=webp&s=9a71bdb2eeeff52b2f8bbb3cf2b678debcd4a060 https://preview.redd.it/0krmqd7bd9ob1.jpg?width=1536&format=pjpg&auto=webp&s=ba3fade0883ee621b185fabc67839db42ea74a53 https://preview.redd.it/8nz00i7bd9ob1.jpg?width=1198&format=pjpg&auto=webp&s=6ddbddf68311f576fbf3c52a47381316feace8c9 ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hi everyone! I'm still experiencing with my own implementation of rag, and I deployed my custom chunking function (honestly don't like the methods on LangChain) . Anyway, I'm searching for alternative methods, algoritms (NLP or not) and models... There are lots of info and different implementation on RAG, but as I can see noone put much effort to augment chunking quality. Also, there are other approach than this one I'm currently using? bi-encoder (instructor) - > cross-encoder (reranking) - > LLM Can someone share some resources, repo, lib or existing implementation of different chunking methods? (or simply discuss here some idea, though or approach) Thanks in advance for you time!! submitted by /u/Distinct-Target7503 [link] [comments]  ( 9 min )

The problem is obtained from Chapter 3 in Wright, Stephen J and Benjamin Recht (2022). Optimization for data analysis. Cambridge University Press I am solving the problem I attach and I have a doubt in section (f). I have solved all the sections (a)-(e). In section (e) I have obtained that I need $$ k \geq \frac{\lambda_{\text{max}}\left(\frac{2}{N} A^TA + 2\mu I\right)}{\lambda_{\text{min}}\left(\frac{2}{N} A^TA + 2\mu I\right)}log((f(x^0)-f(x_\mu)/\epsilon). $$ However in section (f) asks for a tight upper bound but I only can think about the following bound: $f(\hat{x}) \leq f_\mu(x_\mu) + ||\hat{x}||^2+ \epsilon$, which is very simple. Do you think that I can obtain the result in (e) to obtain another bond, or what would you do? submitted by /u/ItsGauss [link] [comments]  ( 9 min )

Need help on how to get started with implementing a research paper. I'm implementing the Imagenet classification task paper for my final year undergrad mini-project. Any advice is appreciated on how to get started? I have mid-level machine learning knowledge and am ready to pick the required concepts on the go. Please help. Thank you :) Link: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf submitted by /u/DrBeans0 [link] [comments]  ( 9 min )

Hi, I'm trying to figure out what is the best way to use LLMs to analyse a very old software entirely written in C. I've tried to to some basic prompts with ChatGPT and it seems to recognise the language. The situation is that I've many .c files with thousands of lines and with a lot of redundant code. Moreover, since there are a lot of data structure with variables names not easily understandable, I need to provide some context to allow the model to trying to understand what the code does. My worry is that providing all the needed info + the file itself (even considering 1 file at a time) I could consume all the model context and therefore not leave room for generating anything of value. Has anyone had the opportunity to face similar problems? Ideas? Cheers Alexio submitted by /u/Alexioc [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2306.04640 GitHub: https://github.com/ibm/moduleformer (under Apache 2.0) Twitter thread: https://twitter.com/Yikang_Shen/status/1702041129267388678 Abstract: Large Language Models (LLMs) have achieved remarkable results. However, existing models are expensive to train and deploy, and it is also difficult to expand their knowledge beyond pre-training data without forgetting previous knowledge. This paper proposes a new neural network architecture, ModuleFormer, that leverages modularity to improve the efficiency and flexibility of large language models. ModuleFormer is based on the Sparse Mixture of Experts (SMoE). Unlike the previous SMoE-based modular language model, which requires domain-labeled data to learn domain-specific experts, ModuleFormer can i…  ( 10 min )

Hey there, I wasn't doing any ml in some time and forgot basics. I was thinking that you may help me. So I trained svc model on small dataset (around 1400 unical records). I have 13 classes, which are badly distributed in the training set and inbalanced. 3 classes took around 80% of all. What the business wants is keep the 3 classes and categorize the rest as "Other" class. In the future they may be able to generate more training data for the remaining labels. How should I do it? I know I can assign everytning to "Other" class, before training with simple if then formula, but dont know if this is the right approach. Any sugestions? I know this may be some case of 1 vs all case, but don't know which exactly. Thanks in advance for any help. submitted by /u/th00masml [link] [comments]  ( 9 min )

Hey everyone! 👋 I’ve been on a bit of a deep-dive lately, trying to catch up on all the awesome stuff that’s been happening in the ML space. It got me wondering, from 2020 to 2023, what have been the absolute must-read papers that shook the foundations and got everyone talking? Whether it’s something that reinvented the wheel in your specific niche or just made waves industry-wide, I wanna hear about it! I’m curious to see how different the responses will be, and hey, this might even become a go-to list for anyone looking to get the lowdown on the hottest trends and discoveries of the past few years. Can’t wait to hear your thoughts! tl;dr I decided to aggregate your best suggestions into categories for anyone interested in reading them without searching through the whole comment se…  ( 10 min )

Hi everyone! I was really excited that Mojo became publicly available and thinking which project can I implement to learn Mojo concepts. Since I have already ported llama2.c to pure Python, I decided why not try to port llama2.py to Mojo now.. And here is what I got First round of llama2.c vs llama2.🔥 battle. Mojo demonstrated 20% better performance than C in a single threaded execution of llama2 inference and 250x times better performance than Python https://i.redd.it/0gcwwfc2r7ob1.gif For reference Mojo is using SIMD vectorization, that's why it's performing great for matmul operations. In the other hand, it turned out that gcc also aggressively optimizes all for loops it can find, so I suggest this benchmark is pretty fair. ​ Mojo natively supports SIMD vectorizations out of the box gcc aggressively vectorizing loops ​ After that I decided to compare both solutions in multi-threaded (parallelized) mode, and now `llama2.c` strike back with help of OMP demonstrating 20% better performance than Mojo ​ https://i.redd.it/gwymffods7ob1.gif I hope this post will be useful for all Machine Learning engineers/enthusiasts/students out there, ensuring we're up-to-date with Modular/Mojo's game-changing AI tech developments. Stay informed and ahead of the curve! Links llama2.🔥: https://github.com/tairov/llama2.mojo llama2.python: https://github.com/tairov/llama2.py llama2.c: https://github.com/karpathy/llama2.c Modular_AI repost in Twitter: https://twitter.com/tairov/status/1701345271752343900 feel the magic on HF: https://huggingface.co/spaces/radames/Gradio-llama2.mojo submitted by /u/Albatross9855 [link] [comments]  ( 9 min )

Hello everyone, I'm currently at a crossroads with a decision that I believe many in this community might have faced or will face at some point: Should I use cloud-based GPU instances like AWS's p3.2xlarge EC2 (with Tesla V100) or invest in building a high-performance rig at home with multiple RTX 4090s for training a large language model? Context: I run a startup and we're currently fine-tuning an open source LLM, and the computational demands are of course high. We want to make an informed choice between using AWS's offerings or setting up a high-performance system at home to start. Cloud Option: AWS p3.2xlarge EC2 Cost: Approximately $3.06 per hour. Specifications: One Tesla V100 GPU, 8 vCPUs, 61 GiB RAM. Pros: Scalability, reliability, specialized software optimizations. Cons: Recurring costs, potential limitations on customization. Home Rig Option: Multiple RTX 4090s Cost: Around $1,600 for each 4090, but I'd own them. Specifications: Even higher TFLOPs than a V100, and memory isn't a constraint (24GB per card). Pros: One-time investment, flexibility, potentially higher raw computational power. Cons: Need to handle cooling, power, and system integration myself I'd love to hear your thoughts, experiences, and recommendations. Here are some specific questions: Performance: How many RTX 4090s would roughly equal the computational power of an AWS p3.2xlarge instance for ML tasks? Cost-Effectiveness: Given that we're a startup with limited resources, does it make more financial sense to invest upfront in hardware? Reliability and Maintenance: For those who have run multi-GPU setups at home, how reliable are they, and what maintenance work is required? Other Considerations: Are there factors I haven't considered that you think are critical? Thank you in advance for sharing your insights! submitted by /u/devolvedai [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2305.16264 GitHub: https://github.com/huggingface/datablations License: All models & code are licensed under Apache 2.0. Filtered datasets are released with the same license as the datasets they stem from. Abstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at this https URL. ​ https://preview.redd.it/ahzyonnqe7ob1.png?width=1015&format=png&auto=webp&s=0e0cb4051e390ea23440cd61bfc0bbf5fce83bb7 https://preview.redd.it/l6a81onqe7ob1.png?width=1014&format=png&auto=webp&s=a36b74cbb510a1f753ef1b891531bb36ab643246 https://preview.redd.it/yyu8h0oqe7ob1.png?width=1001&format=png&auto=webp&s=047cb2bb1932c6215cea0c30e22fd9bbe60391a8 https://preview.redd.it/xskcytnqe7ob1.png?width=1007&format=png&auto=webp&s=4090e92dd9eacb377840327bb7d0ae69ff752b52 submitted by /u/InterviewIntrepid889 [link] [comments]  ( 9 min )

On my team they are using Gradio for LLM applications, etc. When running multiple instances of Gradio applications on the same server, but on different ports, opening a new session is causing an error and closing the previous session, "error" is written in the chat. The logs I found were like "Invalid HTTP request received" and "max retries exceeded". For me, Gradio is supposed to work as a demonstration and not as a scalable product, but they are using it that way and I thought that the problem could be precisely that. But if not, does anyone have any idea what could be going on? No meu time estão utilizando gradio para aplicações de LLMs, etc. Ao executar múltiplas instâncias de aplicações Gradio no mesmo servidor, mas em portas diferentes, a abertura de uma nova sessão está causando erro e encerrando a sessão anterior, fica "erro" escrito no chat. Os logs que encontrei eram como "Invalid HTTP request received" e "max retries exceeded". Para mim, o Gradio é pra funcionar como demonstração e não em forma de produto escalável, mas estão utilizando assim e pensei que o problema poderia ser justamente isso. Mas caso não, alguém tem alguma ideia do que pode estar acontecendo? submitted by /u/Magic_squirrel_hat [link] [comments]  ( 9 min )

Hi, does anyone have a compiled list of non-open source AI models that can be used for MVP building? submitted by /u/Compound_Group [link] [comments]  ( 9 min )

I had a Claude based LLM analyze the strengths and weaknesses of the MetaGPT, ChatDev, AoT, and brain inspired algorithms papers, seeking ways to combine the strengths of two or more of the methods. "Here are some specific examples of how the methods in the Algorithm of Thoughts (AoT), brain-inspired algorithms, MetaGPT, and ChatDev papers could be combined to improve multi-agent systems: MetaGPT could incorporate longer, more elaborate algorithmic examples from AoT into its prompts to guide the LLM's reasoning process. For example, in solving math word problems, the prompt could provide a 4-5 step worked example walking through unpacking the problem statement, setting up equations, solving, and checking the solution. This mirrors AoT's more extensive algorithm narratives. ChatDev's a…  ( 11 min )

submitted by /u/hauntedhivezzz [link] [comments]  ( 8 min )

I asked ChatGPT to talk with Trump's jury about the 3-hour delay. Not bad. ChatGPT: Ladies and gentlemen of the jury, today, we present a compelling case that the three-hour delay in President Trump's response to the events of January 6th is, without a doubt, overwhelming and irrefutable evidence of his planning and conducting the insurrection that unfolded before our eyes. First, consider the timeline. As the violence escalated, President Trump did not immediately call for an end to the chaos. This delay allowed the mob to wreak havoc, endanger lives, and damage our democracy. A leader's duty is to protect the nation and its citizens, and a three-hour wait to act is inexplicable unless there was intent behind it. Second, let's talk about intent. In criminal law, intent matters. It's the difference between an accident and a crime. A deliberate delay in response to an unfolding insurrection suggests a guilty mind. President Trump had the power and the responsibility to intervene immediately. Yet, he chose not to, allowing the violence to escalate. Furthermore, we must consider his words leading up to and during the events of January 6th. His rhetoric could be interpreted as encouragement for his supporters to take matters into their own hands. This, coupled with the delay, paints a picture of a leader who not only knew what was happening but may have orchestrated it. Ladies and gentlemen, we are not here to pass judgment but to seek justice. The evidence of a three-hour delay in responding to the insurrection, along with the surrounding circumstances, presents a compelling case that President Trump planned and conducted this insurrection. It is our duty to hold individuals accountable for their actions, regardless of their position. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

How would today's AI perform as a crime investigator or judge? To find out, I asked Bard the following question: The film Social Network suggests that Mark Zuckerberg stole his idea for Facebook from two brothers who hired him to make an app for the idea. How likely is this to be true? Bard seemed informed and sharp: "The film The Social Network is a dramatization of the founding of Facebook, and as such, it takes some liberties with the truth. However, the film does accurately portray the fact that Mark Zuckerberg was sued by the Winklevoss twins, Cameron and Tyler, who claimed that he stole their idea for a social networking website called HarvardConnection. The Winklevoss twins allege that they met with Zuckerberg in December 2003 to discuss their idea for HarvardConnection, and tha…  ( 10 min )

Hi there! I'm participating in a Hackathon at work and am wondering if this community can give me some direction. Every year our company receives mandates / updates from different parties. In turn, our development teams have to parse through heavy documentation to figure out what needs to change in our code. Ingesting the data is what takes the longest. Our goal is to feed the mandates documentation through an AI and have it return what is needed to be changed in our code. For example, something might say field 200 now needs to include a 6 digit date format vs the 4 digit date format we've had in years past. We have secured a license for Azure AI but honestly no idea if that is the right AI to use. I youtubed a bunch of videos on document processing but I'm also not sure if that is what we are trying to do. Any advice on this is much appreciated. submitted by /u/HillyjoKokoMo [link] [comments]  ( 9 min )

I'm looking for a program that will transcribe live audio playing from my computer (windows). Do you know something like that? I've seen Buzz (https://chidiwilliams.github.io/buzz/docs/usage), but it needs an audio loopback driver in order to work, so I wonder if there are others. 🚀 Bonus points if it recognizes different people talking. 🚀 Extra bonus points if it can transcribe multiple languages. submitted by /u/AleHoju [link] [comments]  ( 9 min )

So today I signed up for a debate on ai. Wheather ai is beneficial or dangerous to human beings. I have the freedom to choose any side. This debate will be watched by about 130 people, all cs freshmen (mind you, also my first time speaking in front of this many people). Now, I'm confident I know more about ai than an average person but I need your help in preparing properly. Which side do I take and what are all the points I should keep in mind? It's 4 pm here and the debate is tomorrow. Any help will be appreciated. Thank you. submitted by /u/CalmGuy69 [link] [comments]  ( 9 min )

Artificial intelligence (AI) has the potential to solve the world's problems or destroy humanity. It is being developed by a few hundred individuals in Silicon Valley, and we have little say in its ethics or existence. AI has already demonstrated creative abilities in painting, writing, and music. It is also being used in drug discovery, therapy, dating apps, and misinformation in politics. The rapid adoption of AI raises concerns about job displacement and the potential for catastrophic events. Experts predict a significant chance of AI causing a catastrophe or even wiping out humanity. Source : https://www.vanityfair.com/news/2023/09/artificial-intelligence-industry-future Summarized by Nuse AI submitted by /u/NuseAI [link] [comments]  ( 9 min )

In less than a year since ChatGPT was introduced, it has become the fastest consumer application to register 100 million monthly active users. But how are consumers using other GenAI products apart from ChatGPT? An a16z Consumer report examines the top 50 GenAI web products (based on SimilarWeb data) to find out. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/f0kh5qag16ob1.png?width=2058&format=png&auto=webp&s=1cab11a31d384c068912c9fca32a000393b795d5 Proprietary models are dominating: 80% of the top 50 GenAI products didn’t exist a year ago—suggesting many of the most compelling consumer experiences are completely novel. Interestingly, 48% of these are bootstrapped—with no outside funding. ChatGPT holds a massive lead: ChatGPT alone accounts for 60% of the entire list's monthly traffic, with roughly 1.6 billion visits and 200 million monthly users as of June 2023. LLM assistants are dominating: LLMs, including Google’s Bard and Quora’s Poe, constitute 68% of total consumer traffic to the top 50. The other categories seeing significant traffic are AI companions and content-generation tools. GenAI marketing is mostly organic: Marketing for most of these products has been reliant on referrals, word of mouth, and other traditional marketing as they enter the market. About 90% of these companies are already monetizing, and most do so via a subscription model. GenAI and mobile adaptability: Given the extensive consumer time spent on mobile, an increase in mobile-first GenAI products is expected as the technology evolves. (source) P.S. If you like this type of analysis, sign up for my free newsletter that deciphers the fastest-moving news and research in AI and tech. Professionals from Google, Meta, and OpenAI are already on board. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Adobe’s Firefly generative AI tools are now widely available.[1] Stability AI, gunning for a hit, launches an AI-powered music generator.[2] Elon Musk warned of civilizational risks posed by artificial intelligence. Sundar Pichai of Google highlighted the technology’s potential to solve health and energy problems. And Mark Zuckerberg of Meta stressed the importance of open and transparent A.I. systems.[3] German military plows millions into AI ‘environment’ for weapons tests that could change combat forever.[4] Amazon launches generative AI to help sellers write product descriptions.[5] Sources: [1] https://www.theverge.com/2023/9/13/23871537/adobe-firefly-generative-ai-model-general-availability-launch-date-price [2] https://techcrunch.com/2023/09/13/stability-ai-gunning-for-a-hit-launches-an-ai-powered-music-generator/ [3] https://www.nytimes.com/2023/09/13/technology/silicon-valley-ai-washington-schumer.html [4] https://www.foxnews.com/world/german-military-plows-millions-ai-environment-weapons-tests-change-combat [5] https://www.aboutamazon.com/news/small-business/amazon-sellers-generative-ai-tool submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

My experience in CS, coding, and programming is very minimal. I understand general concepts but only through the lens of a degree in physics that required I mess around with WolframAlpha sometimes (which I really enjoyed). I've been considering getting a second degree in CS or something related but want to find a clear(ish) path before committing to it. I would love to hear any related thoughts as well! ​ Prompt Engineering seems like a pretty fresh field of study. Is it up and coming as a career path? People that specialize in this, what field(s) did you come from and how would you recommend diving into it? Considering my experience, would I be able to actually find work, freelancing or employed? How on earth do I get started in this world? It seems so insanely big and complicated but I am just fascinated by the idea of using written dialogue to manipulate the output of an LLM! From my very high overview of PE and the recent advances in AI, PE as a field of study and interest is going to expand exponentially, is that accurate? submitted by /u/Top_Room_6714 [link] [comments]  ( 9 min )

Posted by Gabriel Barcik and Duc-Hieu Tran, Research Engineers, Google Research In today's digital age, smartphones and desktop web browsers serve as the primary tools for accessing news and information. However, the proliferation of website clutter — encompassing complex layouts, navigation elements, and extraneous links — significantly impairs both the reading experience and article navigation. This issue is particularly acute for individuals with accessibility requirements. To improve the user experience and make reading more accessible, Android and Chrome users may leverage the Reading Mode feature, which enhances accessibility by processing webpages to allow customizable contrast, adjustable text size, more legible fonts, and to enable text-to-speech utilities. Additionally, An…  ( 93 min )

I'm trying to build a humanoid model and then train it to perform some tasks , I have decided to go with mujoco for the simulation and now I'm wondering if I should use the C++ API or the python one. the python implementation says it uses C API but is it good? Also if it's slower than the c++ one how slow is it? I'll probably have to make something real time and hence can't compromise much on the speed, but if it's only and small amount it's acceptable. would really appreciate some guidance in this matter thank you submitted by /u/rakk109 [link] [comments]  ( 9 min )

Hello, I am looking for environments to test out some ideas I have for algorithmic pricing. By algorithmic pricing environments, I mean there are multiple competing algorithms trying to maximize profits. I can't really find any out of the box implementations. There are trading environments but those are not what I am looking for. Any help would be appreciated, thanks. submitted by /u/Next_Gap8224 [link] [comments]  ( 9 min )

Hi! Recently, I tried to follow fetch_pick_and_place.env in gymnasium_robotics to build a similar environment with Franka. I found that the core of this implementation is to use the mocap to control the end-effector, and then mocap derives joint angles using the built-in inverse kinematics algorithm. For the fetch_pick_and_place.env, mocap does not cause mutations and oscillations in configuration space. However, when I use mocap to control Franka, oscillations in joint space occur frequently, although I've minimized the step size of the mocap to ensure that the movement of the end-effector in Cartesian space is minor. Fetch and Franka are both redundant arms, I don't know why there is such a big difference in mocap performance. Here is the video to illustrate the above phenomenon Franka I've opened issues on mujoco and gymnasium robotics repositories, but it didn't initiate any discussion. Any help would be appreciated! Thanks! ​ submitted by /u/UpperSearch4172 [link] [comments]  ( 9 min )

In first part of this multi-series blog post, you will learn how to create a scalable training pipeline and prepare training data for Comprehend Custom Classification models. We will introduce a custom classifier training pipeline that can be deployed in your AWS account with few clicks.  ( 10 min )

Today, generative AI models cover a variety of tasks from text summarization, Q&A, and image and video generation. To improve the quality of output, approaches like n-short learning, Prompt engineering, Retrieval Augmented Generation (RAG) and fine tuning are used. Fine-tuning allows you to adjust these generative AI models to achieve improved performance on your domain-specific […]  ( 8 min )

This post takes you through the most common challenges that customers face when searching internal documents, and gives you concrete guidance on how AWS services can be used to create a generative AI conversational bot that makes internal information more useful. Unstructured data accounts for 80% of all the data found within organizations, consisting of […]  ( 14 min )

submitted by /u/pablocarrera [link] [comments]  ( 8 min )

Suppose m = pq where p and q are large, distinct primes. In the previous post we said that calculations mod m can often be carried out more efficiently by working mod p and mod q, then combining the results to get back to a result mod m. The Chinese Remainder Theorem assures us that […] Chinese Remainder Theorem synthesis algorithm first appeared on John D. Cook.  ( 5 min )

Suppose m is a large integer that you are able to factor. To keep things simple, suppose m = pq where p and q are distinct primes; everything in this post generalizes easily to the case of m having more than two factors. You can carry out calculations mod m more efficiently by carrying out […] Gaining efficiency by working modulo factors first appeared on John D. Cook.  ( 5 min )

RSA encryption a map from numbers mod n to numbers mod n where n is a public key. A message is represented as an integer m and is encrypted by computing c = me mod n where e is part of the public key. In practice, e is usually 65537 though it does not have […] Group theory and RSA encryption first appeared on John D. Cook.  ( 7 min )

Not all messages encrypted with the RSA algorithm can be decrypted. This post will show why this is possible and why it does not matter in practice. RSA in a nutshell RSA encryption starts by finding two large primes, p and q. These primes are kept secret, but their product n = pq is made public. […] RSA encrypted messages that cannot be decrypted first appeared on John D. Cook.  ( 6 min )

Modern applications heavily rely on robust network infrastructure, requiring continuous innovation. In this evolving landscape, Microsoft is at the forefront, spearheading innovation efforts in networking and strengthening the foundational network infrastructure that underpins the cloud ecosystem. By investing in and enhancing this critical infrastructure, Microsoft not only ensures the resilience and scalability of cloud services […] The post Microsoft at ACM SIGCOMM 2023: Innovating the future of networking appeared first on Microsoft Research.  ( 10 min )

What’s the driving force behind AI’s recent, rapid progress? Research manager Ahmed Awadallah shares his insights on this, the two-stage approach to training large-scale models, and the need for better model evaluation in this episode of the #MSRPodcast. The post AI Frontiers: The future of scale with Ahmed Awadallah and Ashley Llorens appeared first on Microsoft Research.  ( 31 min )

Working as a data scientist is the dream of many IT professionals these days. It is no secret that data science is a skyrocketing field attracting young professionals and inspiring many to switch careers to data science. On one front are young professionals who study their courses in colleges to pursue their dream of becoming… Read More »Are data science certifications the gateway to competitive pay? The post Are data science certifications the gateway to competitive pay? appeared first on Data Science Central.  ( 19 min )

CUPED: Improve Your A/B Testing - Detect Smaller Gains, Utilise Smaller Samples and Make Smarter Decisions! The post CUPED for starters: Enhancing controlled experiments with pre-experiment data appeared first on Data Science Central.  ( 26 min )

Data centers are known for their impact on the environment. They run 24/7 and exude a lot of heat. Massive warehouses full of hot technology require advanced cooling systems or an HVAC system pushed to its limit.  Data center managers and sustainability leaders no longer settle for antiquated techniques. They’re striving to develop greener and… Read More »Searching for sustainability in data center cooling The post Searching for sustainability in data center cooling appeared first on Data Science Central.  ( 20 min )

The best way to model business and consumer dynamics is collaboratively, with stakeholders all in the same virtual room contributing. Of course, this has been happening asynchronously for some time now, but the potential exists for more real-time interaction.  Modelers don’t work in a vacuum, of course. The iterations between a modeler who develops a… Read More »Collaborative visual knowledge graph modeling at the system level The post Collaborative visual knowledge graph modeling at the system level appeared first on Data Science Central.  ( 20 min )

GFN Thursday is downright demonic, as Devil May Cry 5 comes to GeForce NOW. Capcom’s action-packed third-person brawler leads 15 titles joining the GeForce NOW library this week, including Gears Tactics and The Crew Motorfest. It’s also the last week to take on the Ultimate KovaaK’s Challenge. Get on the leaderboard today for a chance Read article >  ( 6 min )

The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.  ( 10 min )

Has anyone ever encountered this error while working with the OpenGL library? raise ImportError("Unable to load OpenGL library", *err.args) ImportError: ('Unable to load OpenGL library', "Could not find module 'OSMesa' (or one of its dependencies) I have done pip install opengl and pyopengl, but it doesn't seem to solve the problem. Specifically, I am trying to run this code: https://github.com/brjathu/LART submitted by /u/BigDreamx [link] [comments]  ( 9 min )

Hi all, I'm new to machine learning and have decided to use a combination of Stable baselines3 and Wandb. I'm at the point where I'm running sweeps using Wandb and want to utilize another PC I have laying around to run agents on. What is the best way to get my python code for the agent to run onto the spare PC? I know I can manually load copy the code over, but I'm looking for a more efficient method that will maintain any changes made to the source code. Maybe packaging up the python code within the sweep and having the agent download and execute it? I'm not all too familiar with possibilities and limits of trying to achieve this so any and all input is appreciated. Thanks! submitted by /u/chip_fork [link] [comments]  ( 9 min )

I have this question that I cannot seem to settle in my head. All papers that I read, report the average (std) performance results across each folds when they report F1, Precision, etc.. Somebody that I highly trust in ML (somebody with a PhD in the field) was reporting the results after saving all the predicted labels (y_pred) and actual labels (y_true) to a list and compute the F1 score one time with the pooled predictions. I now am working on a dataset (binary classification) and trying to validate my model using leave-one-subject-out CV (Some people in my dataset have more lines than others). When I take the average of all iterations I get poor results (F1 score= 0.5), but if I pool all the predictions and compute the F1 score at the end, I achieve decent performance (F1 score =0.7). So, in my project, it is in my best interest to use the second approach, and somebody that I trust tells me that it is okay to do this approach. But I cannot seem to find a paper that says that this approach is acceptable or good. What do you guys think and do you have any suggestions? submitted by /u/enthusiastic31 [link] [comments]  ( 9 min )

I'm working on a project that involves analyzing large samples of Java codes. My end goal is to perform classification based on these codes. For this, I've been trying to efficiently encode the full names of the methods (in the package.class.methodname format) in the Java code. Currently, I am experimenting with doc2vec. I'm treating the components of each method's full name (separated by dots) as individual documents. This allows me to produce vectors for each method name, and I evaluate the results by computing the cosine similarity between pairs of similar method names. The results were not good so far. Before moving to doc2vec, I tried using an LLM which gave me good results. However, the inference time was far too long, especially given the scale at which I'm working. I also considered using a Bag of Words model, but quickly realized it wouldn't be effective. Many of the method names in my samples are obfuscated, making this approach unsuitable. The issue I'm facing is that using the direct method names as features is not generalizing well for classification. A slight change in a method's name results in losing that particular feature, making my model fragile. What are some optimal encoding methods for small sentences (around 5-6 words) like these method names, that can scale well? Also, Are there any specialized encoding techniques tailored for software code that I could use for this task? Any suggestions or insights would be really helpful. Thank you! submitted by /u/Practical_Mango_8720 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.06180 Github: https://github.com/vllm-project/vllm Blog: https://vllm.ai/ Abstract: High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. https://preview.redd.it/x8w8ckejv2ob1.jpg?width=667&format=pjpg&auto=webp&s=28fae778b67ac28fc72d084f071b12c92cb5ea07 https://preview.redd.it/ctlrqpejv2ob1.jpg?width=1468&format=pjpg&auto=webp&s=31755d169673ee5d30efa3f05bd6cb10813b328d https://preview.redd.it/z5r7knejv2ob1.jpg?width=1504&format=pjpg&auto=webp&s=9ceb5370aa5a7cc0688fe9a3771a0328262c3a01 ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Problem: Given a database of the most viral tweets (of a certain shared category), I am hoping to use LLM's to generate further viral tweets. Currently I am seeing this as a synthetic data generation problem: two approaches I am thinking of is 1) grounding (using viral examples to guide the prompt) and 2) filtering (finetuning an LLM to predict virality and filtering for the most viral generations) I want to ensure that the output retains the "viral" structure/style and is diverse/new (i.e no copies). Any general directions or references are appreciated submitted by /u/greatSWE [link] [comments]  ( 9 min )

I'm currently doing Integrated MSc in Mathematics (in India) and until now I've done a remote research intern in a French university and I'll do a research intern at a French research laboratory (INRIA-LORIA) next year, I want a job (in India) at the R&D sector (Data Scientist or ML Engineering anything would do) Idk if research interns are as valuable as Industry internships when it comes to R&D? Basically after my masters, I'll probably have only these two internships to show as a work experience, Probably won't be able to get an internship in an IT company unless i opt for remote work (which is also not guaranteed atm). Mu question, generally is Msc + 2 research internships + 1or 2 publications good enough to secure a decent job in R&D? submitted by /u/Emotional-Zebra5359 [link] [comments]  ( 9 min )

Hello!! I have a BSc in Mathematics and currently I'm going to start a MSc in FinTech ( it has 3 courses out of 9 that have to do with ML, NN and many more ). Since I am really fascinated about programming ( didn't like it when I was at Maths department because of teachers and I am starting learning on my own through courses and we will have many programming languages in my masters degree ex. R,Python, SQL and others) and I would start as a data scientist at the beginning, could I through years of experience later ( ideally 1-2 years later, provided that I have a solid and good programming skills and projects) become a ML engineer? ( Now of course I can't become one because I know that it is difficult since I am competing with people that have CS degrees). submitted by /u/math-is-cool-62 [link] [comments]  ( 9 min )

Hi there! I bring 2 years of experience from a European investment fund and a solid idea for a B2B SaaS solution targeting private equity investment funds. This market is notoriously challenging to penetrate without insider knowledge. The timing couldn't be better. Similar kind of software is currently sold €20k per user by a semi-monopolistic boomer company that is ready to be disrupted. Offer: - Equity shared equally. - Ready to quit my job and go full-time on it, if I find the right co-founder. Ideal Partner: Seeking someone proficient in SQL for handling large datasets and able to integrate OpenAI's API within such datasets (technical AI skills required). While I'm based in Berlin and prefer a European co-founder, it's not mandatory, but meeting in person is a must before we commit. Don't be afraid to DM me if intrigued! Together, we can make it happen. Let's revolutionize the sector! submitted by /u/Sudden_Possible489 [link] [comments]  ( 9 min )

İ am lookong for the pdfs about machine learning, maths for ML, ml projects. İs there any sites i can find pdf like that? submitted by /u/Necessary-Car-5080 [link] [comments]  ( 9 min )

Hey, I've been using TF pretty much my whole deep learning career starting in 2017. I've also used it on Windows the entire time. This was never a major issue. Now when I tried (somewhat belatedly) upgrading from 2.10 to 2.13, I see the GPU isnt being utilized and upon further digging see that they dropped Windows GPU support after 2.10: "Caution: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow or tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin" This is really upsetting! Most of the ML developers I know actually use Windows machines since we develop locally and only switch to Linux for deployment. I know WSL is an option, but it (1) can only use 50% RAM (2) doesnt use the native file system. I feel very betrayed. After sticking with, and even advocating for Tensorflow when everyone was (and still is) switching to PyTorch, TF dropped me! This is probably the final nail in the coffin for me. I will be switching to PyTorch as soon as I can :-( -Disgruntled user submitted by /u/rsandler [link] [comments]  ( 9 min )

https://research.sc/participant/login/dynamic/A1D66883-6E8F-409B-8EF9-AC989A76C7E9 Psychology researchers at Swansea University are carrying out an original study to see whether artificial intelligence is able to generate infant faces between the ages of 0 and 7 years. AI generated pictures will be presented alongside real faces from an existing face database, with a sliding scale underneath that you will use to roughly estimate the age of the child’s face. The experiment should only take between 15 and 20 minutes Thank you for your time. submitted by /u/Logipsychlical [link] [comments]  ( 9 min )

So I'll be presenting a talk on Data pre-processing in deep learning in my city's Keras Community Day, and I am still thinking about all the content I want to present there. What I want from this session is to present different ways of preprocessing the data for a deep learning model. I want to show different types of implementations, how those affect the final trained model, when to use which type of data preprocessing and things similar to this. It would be great if you can suggest me some topic, notebooks or datasets for the same. All the notebooks that show good implementation and affect of data preprocessing are absolutely welcome. Also, as this is **Keras** Community Day, I'll have to include more about data preprocessing using Keras and less about other libraries. Also, if you could help me with this: I am confused between showing preprocessing using layers or doing the preprocessing without layers. I know this sounds vague, but if you have any idea about this, let me know. Thank you for reading! submitted by /u/inclinedadarsh [link] [comments]  ( 9 min )

I'm currently in my first year of MSc. in Engineering Mathematics and Computational Science. First Study Period (Currently) I have nonlinear optimization and High-performance computing. The track I want to choose is a mix between Machine Learning and Big Data. I can select 2 courses for Study Period 2. Here are the potential courses to select from: Game Theory and Rationality Large-Scale Optimization Advanced Probabilistic machine learning Basic Stochastic Processes Options and Mathematics (Options trading) Foundations of Probability Theory ​ I need to select 4 potential courses and rank them from high preference to low preference. In case I don't get one of them, the other will be preferred. Please Machine Learning Reddit Gods, Help me. submitted by /u/AdMaster9439 [link] [comments]  ( 9 min )

Hi r/MachineLearning, TL;DR: Run AI apps on pay-per-second cloud GPUs that hot reload with your code changes. Documentation: https://docs.beam.cloud I’m Eli, and my co-founder and I built Beam to run workloads on serverless cloud GPUs with hot reloading, autoscaling, and (of course) fast cold start. You don’t need Docker or AWS to use it, and everyone who signs up gets 10 hours of free GPU credit to try it out. Here a few examples of things you can run on Beam: Fine-tune a LLaMA LLM Transcribe videos with Whisper Train a custom stable diffusion model Beam is built for a fast developer experience. We’ve felt that using Docker and AWS directly is too slow for iterative development. You’ll often find yourself making changes to your code and waiting 10 minutes for a new image to bu…  ( 10 min )

Hey r/machinelearning! I'm thrilled and anxious to share an early version of the MLflow Plugin Manager. It's designed to simplify your mlflow installation, allowing you to install, update, and uninstall MLflow plugins directly from the web interface. Think of it as the "wbond's package manager for sublime", but tailored for MLflow! 📽️ https://i.redd.it/9gj8vqcz01ob1.gif ​ Yes, it's in its infant stages and doesn't boast of a fancy UI yet, but I'm eager to get your feedback! 🔍 What are your first impressions? Is this a good idea? 💡 Any features you'd love to see? 🌐 Ideas on promoting or expanding its reach? I built this to bring a bit more ease to our community. Can't wait to hear your thoughts and where we can take this next! Repo: https://github.com/thijsdezoete/mlflow-plugin-manager/ submitted by /u/jessepnk [link] [comments]  ( 9 min )

Hi all, Someone asked me today "why are LLMs still non deterministic in their output when temperature is set to 0. Assume fixed model between runs on the same machine" I was like WTF are you saying - the randomness in LLM comes from temperature - chat gpt etc.. might have other randomness in the process but we don't have exact info on this. What I know is that in a standard transformers architecture, temperature is the only parameter that can enduce non deterministic behaviour at inference time. He was convinced that there was more to it "i spoke about this to other LLM experts and they also are not sure" I'm like wtf - I start looking up online and do find some people who claim that temperature is not the only thing that influences stochasticity during inference, but I can't find an answer as to what it is exactly. Anyone has a clue of what I am missing here? Thanks! submitted by /u/WagnerianJLC [link] [comments]  ( 9 min )

​ A composite of specialized Tsetlin machines that enables plug-and-play collaboration. I have a love-and-hate relationship with CIFAR-10/100. I love the datasets for the challenge. On the other hand, they are two datasets where Tsetlin machines have struggled with getting state-of-the-art performance. (The Tsetlin machine is a low-energy logic-based alternative to deep learning that has done well on MNIST, Fashion-MNIST, CIFAR-2, and various NLP tasks.) I have been working for some time now on figuring out a solution, and this summer, I finally had a breakthrough: a new architecture that allows multiple Tsetlin machines to collaborate in a plug-and-play manner, forming a Tsetlin machine composite. The collaboration relies on a Tsetlin machine's ability to specialize during learning and…  ( 10 min )

Hello everyone! Have you ever thought about how to create an object detection system using YOLO that works with any game? If you're interested, I've created a tutorial with all the steps to develop this system. I'll leave a link to the video where I demonstrate the process step by step using the game Diablo 2 as an example. I hope you enjoy it, and if you have any suggestions, feel free to send a message or comment here! link to the tutorial: https://www.linkedin.com/posts/moisesdias_english-version-below-tutorial-crie-activity-7107686497885011969-ZLVW/ submitted by /u/moisesdepaulodias [link] [comments]  ( 9 min )

A curated reading list for the adversarial perspective in deep reinforcement learning. https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning submitted by /u/ml_dnn [link] [comments]  ( 9 min )

Can a single layer neural network solve the XOR problem? Most answers say no, but with this one weird trick the answer is yes! And we don't even need a bias! The trick is to multiply the outputs of a single (2,2) linear layer. Here is how: class XorSolver(nn.Module): def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.layer = nn.Linear(2, 2, bias=False) # we don't even need a bias! def forward(self, x: torch.Tensor) -> torch.Tensor: z = self.layer.forward(x) y = z[:, 0] * z[:, 1] return y This is the loss and model output after 5000 epochs: loss: 6.516383166399464e-08 Input: [[0. 0.] [0. 1.] [1. 0.] [1. 1.]] Model output: [0. 1. 1. 0.] Expected output: [0. 1. 1. 0.] Layer weight: [[-1.248097 1.2202195 ] [-0.80121976 0.81952316]] The full implementation with training and inference - around 50 lines of code - can be found on GitHub. Why it works? Basically the model simulates a more sophisticated neuron which allows more interactions between the inputs. By multiplying the outputs of two neurons, we introduce a form of non-linearity that allows us to separate data that are not linearly separable, like in the XOR problem: f(x1, x2, w1, w2, w3, w4) = (x1 * w1 + x2 * w2) * (x1 * w3 + x2 * w4) w1, w2 are learnable parameters of the first neuron w3, w4 are learnable parameters of the second neuron x1, x2 are inputs to the model Related studies Solving XOR with a single Perceptron Artificial Neural Networks With Adaptive Polynomial Activation Function Single Cortical Neurons as Deep Artificial Neural Networks Dendritic action potentials and computation in human layer 2/3 cortical neurons submitted by /u/tecbar [link] [comments]  ( 9 min )

Hello guys. As I plan on soing a scientific research project, I would need some cloud compute. Say for a month of usage(can I rent for month?). What are some popular options? I am looking at something with 48gb vram pooled,mybe a600 or a100 and some decent cpu, and 2tb space. submitted by /u/Outrageous_Ad1452 [link] [comments]  ( 9 min )

Currently I'm training medium (1B-3B) sized audio models. I have several different architectures in mind. Obviously I don't want to train the full-sized models and then compare them, thats a waste of money. So I'm thinking of training smaller versions (~100M) and then comparing those instead. My question is there some sort of best practice for this? Some smaller multiple of your full model size where it is best to compare? Thanks. submitted by /u/ginger_turmeric [link] [comments]  ( 9 min )

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Hi, I would love to create a small group of people who work together in AI. The project would be to create an AI that can infer new novel knowledge from existing datasets, as opposed to be being limited by operating within the training data. Specifically to be used in the quest to learn more about the universe. So I am looking for a team of likeminded individuals who want to grow in the field of AI. I'd love to setup a discord, subreddit and github profile to showcase our work. My introduction question is: How do we get AI's to expand upon current knowledge instead of just serving from the knowledge itself. Anyone interested in joining me in this? submitted by /u/Miserable-Cobbler-16 [link] [comments]  ( 9 min )

Eight big names in tech, including Nvidia, Palantir, and Adobe, have agreed to red team their AI applications before they're released and prioritize research that will make their systems more trustworthy. The White House has secured voluntary commitments from Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, and Stability AI to develop machine-learning software and models in a safe, secure, and trustworthy way. The commitments only cover future generative AI models. Each of the corporations has promised to submit their software to internal and external audits, where independent experts can attack the models to see how they can be misused. The organizations agreed to safeguard their intellectual property and make sure things like the weights of their proprietary neural networks don't leak, while giving users a way to easily report vulnerabilities or bugs. All eight companies agreed to focus on research to investigate societal and civil risks AI might pose if they lead to discriminatory decision-making or have weaknesses in data privacy. The US government wants Big Tech to develop watermarking techniques that can identify AI-generated content. The US has asked the corporations to commit to building models for good, such as fighting climate change or improving healthcare. Source : https://www.theregister.com/2023/09/12/nvidia_adobe_palantir_ai_safety/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/Raymondlkj [link] [comments]  ( 9 min )

I’ve been using the paid version of GPT for a while but I think it’s time to move on. For $20 a month i would at least like for there to be an option to input an image, but I would also maybe pay a bit more than that per month for a suite of tools or something if it could also do image generation in addition to just text. I’m sorry if it seems like I should be able to understand anything better - please note I am disabled, my use of the tools is personal (creative and household) and not professional, and I’m doing my best by asking here. I do not mean to bother anyone with my own ignorance. Thank you. submitted by /u/CaveLady3000 [link] [comments]  ( 9 min )

Project Gutenberg and Microsoft have created thousands of free audiobooks that use neural text-to-speech technology to generate the voices.[1] A group of U.S. authors, including Pulitzer Prize winner Michael Chabon, has sued OpenAI in federal court in San Francisco, accusing the Microsoft-backed program of misusing their writing to train its popular artificial intelligence-powered chatbot ChatGPT.[2] Numenta launches brain-based NuPIC to make AI processing up to 100 times more efficient.[3] Adept AI Labs released Persimmon-8B. Persimmon-8B is an open-source, fully permissively licensed model in the 8B class. This model holds immense potential for a wide array of applications, aiming to assist users in various computer-related tasks.[4] Sources: [1] https://www.zdnet.com/article/heres-how-to-access-thousands-of-free-audiobooks-thanks-to-microsoft-ai-and-project-gutenberg/ [2] https://www.reuters.com/technology/more-writers-sue-openai-copyright-infringement-over-ai-training-2023-09-11/ [3] https://venturebeat.com/ai/numenta-launches-brain-based-nupic-to-make-ai-processing-up-to-100-times-more-efficient/ [4] https://www.marktechpost.com/2023/09/09/adept-ai-labs-open-sources-persimmon-8b-a-powerful-fully-permissively-licensed-language-model-with/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I see a ton of AI image tools out there. Some let you upload image files and modulate/modify them in some way. I am wondering if a tool exists that will take a real life product image and make it appear more like a render/computer generated image. Essential I would love to be able to take a pic of a product and use automatic smoothing and AI simulated rendering to output a clean image that looks like a 3d render. This would be used as a product image for an e-commerce website. submitted by /u/ElonMusk0fficial [link] [comments]  ( 9 min )

Alibaba Cloud has open sourced two of its generative AI models, Qwen-7B and Qwen-7B-Chat, for commercial and research use. The models' codes and documentation will be accessible through Alibaba Cloud's AI model repository ModelScope and the US collaborative AI platform Hugging Face. Companies with fewer than 100 million monthly active users can use the models for commercial purposes free of charge, while those with more users will need to request a license. Alibaba aims to democratize AI technology and support LLM start-ups. Alibaba Cloud's ModelScope platform currently features over 1,000 ready-to-use AI models contributed by 20 leading AI institutions. Source : https://www.scmp.com/tech/big-tech/article/3229907/alibaba-cloud-open-sources-its-two-generative-ai-models-based-chatgpt-style-tongyi-qianwen submitted by /u/NuseAI [link] [comments]  ( 9 min )

I have a text script that I want to turn into a video. For the sake of context, the video is on balancing a person’s daily activities. I’m getting tired trying to find matching stock footages for the videos. I was wondering if there is a way to do this using AI tools? Synthesia won’t do because it looks like a video narration, more than a video essay. Any suggestions would help. Thanks in advanced! submitted by /u/Entaro2109 [link] [comments]  ( 9 min )

Hi everyone, I'm hunting for a HumanEval Benchmark for Google's Codey model and am having a tough time hunting it down. Can anyone point me to an Arxiv paper or a coding leaderboard that includes Codey? Thanks! submitted by /u/Iamreason [link] [comments]  ( 9 min )

But product & tech teams succumb to the pressure and move on to developing proof of concepts & even launch products that fail to achieve ROI Why? 1- use cases are not well defined 2- not enough data or right data strategy 3- data and model architecture not founded well I love Apple’s approach to AI, they shy away from the hype and focus on the fundamentals. First the customer, product, then the tech that will add the value the customer needs. What do you think are the top reasons generative AI applications succeed? View Poll submitted by /u/AILaunchpad [link] [comments]  ( 9 min )

I have an open book exam that has a lot of information that I will need to physically search through. Is there a way i can load all he PDFS and create a customised chatgbt style search, so i can easily look through all the information and research i have? submitted by /u/yellowmushroom22 [link] [comments]  ( 9 min )

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I wanna develop small scale personal AI apps for each my friends and AI said i should learn Python, TensorFlow Lite, SQLite, GCP, Pandas, Scikit Learn and Keras. How right is this? submitted by /u/Leading-Ad2278 [link] [comments]  ( 9 min )

Tests carried out by Logically confirm these platforms accept 85% of prompts tailored for election manipulation. submitted by /u/Asleep-Television-24 [link] [comments]  ( 9 min )

AI Chatbots, such as OpenAI's ChatGPT, can create incredibly cost-friendly software in record time, reveals a new study. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/rxpr6db3aynb1.png?width=1300&format=png&auto=webp&s=721ff5d8f9d25b5e48fa26e2b335c1d20620a83a The AI Tech Company Experiment Brown University and several Chinese University researchers put ChatGPT-powered AI bots to the test by making them run a hypothetical software development company, ChatDev. The AI chatbots were given specific roles and allocated respective stages based on the traditional waterfall model encompassing designing, coding, testing, and documenting. AI 'employees' functioned with minimal human input to complete their parts of the software development process. Impressive Results Assigning 70 tasks to ChatDev led to the completion of the entire software development process in under seven minutes at a cost of less than one dollar on average. A stunning 86.66% of the generated software systems performed flawlessly. Despite some language model errors and biases, the study demonstrates AI's immense potential in automating tasks - a boon, especially to junior programmers around the world. Broader Implications Powerfully generative AI technologies like ChatGPT can perform specific job functions, saving time, and boosting productivity in several industries. While coders find such tools beneficial, it's also critical to note that limitations and biases do exist in AI models which could potentially affect the software creation process. (source) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most vital news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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>Sailea is a student run non-profit that does not charge for any of its services 🌟 Join SAILea’s Free Webinar with Dr. Richard Marks! 🌟 🗓️ Date: September 23rd, 2023 ⏰ Time: 3:00-4:00PM EST Don't miss an exclusive opportunity to learn from an AI expert! Join us for a free webinar featuring Dr. Richard Marks, a renowned CS and Data Science professor at UNC-Chapel Hill University with a remarkable journey – from Google to PlayStation, and the mind behind EyeToy and PlayStation Move. 🚀 What to Expect: 🔹 Deep insights into tech innovation. 🔹 Career advice. 🔹 Live Q&A with Dr. Richard Marks. Reserve your spot now: sailea.org/events 🔥 Don’t miss this opportunity! Register today!🔥 submitted by /u/Envoy-Insc [link] [comments]  ( 9 min )

I was thinking of some major challenges in modeling a generic real-world environment. Some of them are: adaptive agents, uncertain intentions, and lack of common knowledge. However, most of the papers I see on RL make some assumption or other violating one or more of these, like considering simple agents, assuming known knowledge about others' intentions, and considering that the models of other agents are known when irl an agent hardly has a model of other agents it interacts with apriori. Consider an airport scenario where agents are trying to get into respective planes within a given time, and the gates to each plane allow one person at a time. Looking at the scenario from the view of a single agent, they know what they want, but they can't really make any assumptions about the intentions, strategy, and complexity of other agents beforehand. These other agents can be neutral or adversarial (competing for getting in the same plane) from the agent's viewpoint. All they can see is a restricted view of the motions of some of the other agents. What would you say could model and provide a solution in such a scenario? It is to be noted that other agents can change their strategies based on actions taken by you till now, and so can you. Due to having incomplete information, I fail to see the notion of an equilibrium, and the agents needn't be fully rational as well. submitted by /u/Quirky_Concoction [link] [comments]  ( 9 min )

https://github.com/lowrollr/turbozero I've recently been working on TurboZero, a vectorized implementation of AlphaZero where model inference, search (MCTS), and environment simulation all run in parallel on the GPU. I've also implemented a self-contained training/evaluation pipeline, along with a few environments. I've written a wiki and a starter notebook for those who want to dig deeper. This project is similar to DeepMind's mctx, but supports MCTS subtree persistence (unnecessary for MuZero, which is what mctx was mainly built to support), is written with PyTorch rather than JAX, and can also stand on its own and train models end-to-end. I hope to continue to expand and improve upon this as time allows, and I hope someone here might find it useful or interesting! This is my first major open-source project of any real substance and I still don't have tons of experience with RL, so any feedback/advice is greatly appreciated. submitted by /u/lowrollr [link] [comments]  ( 9 min )

Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.  ( 10 min )

Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.  ( 10 min )

When he isn’t investigating human motor control, the graduate student gives back by volunteering with programs that helped him grow as a researcher.  ( 10 min )

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content. […]  ( 8 min )

In this issue: Efficient polyglot analytics on semantic data aids query performance; generative retrieval for conversational question answering improves dialogue-based interfaces; a new tool uses ML to address capacity degradation in lithium-ion batteries. The post Research Focus: Week of September 11, 2023 appeared first on Microsoft Research.  ( 9 min )

The new #MSRPodcast series “Abstracts” is your source for cutting-edge research in brief. In the first episode, join researchers Ava Amini and Kevin K. Yang to learn about their new paper on using evolutionary-scale protein data to improve protein design. The post Abstracts: September 13, 2023 appeared first on Microsoft Research.  ( 13 min )

Generative AI-based models can not only learn and understand natural languages — they can learn the very language of nature itself, presenting new possibilities for scientific research. Anima Anandkumar, Bren Professor at Caltech and senior director of AI research at NVIDIA, was recently invited to speak at the President’s Council of Advisors on Science and Read article >  ( 5 min )

submitted by /u/Sollimann [link] [comments]  ( 9 min )

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So, I've been working on an AI data marketplace platform for a few months now. Users can buy, sell, request, and subscribe to data/datasets (and soon even train their ML/AI models using other users' datasets). One of our key features is the request feature, which allows users to submit data requests for free. These requests include descriptions, required fields, geographical scope, budget etc... Once a request is posted, it's sent to numerous companies, organizations, and data vendors that have the potential to fulfill it. I understand how frustrating the data acquisition process can be, so I designed this platform to be your one-stop shop for all data-related transactions. You no longer have to spend weeks or months dealing with different vendors and companies through slow emails. With our platform, you can request, negotiate, and purchase data all in one place, and it's completely free to post a request, by the way. We've already achieved some successes, and we hope to help more people access the datasets they need. After all, the best AI models are built on diverse and differentiating data. We've had some notable achievements, and we're eager to see if we can fulfill even more interesting dataset requests! submitted by /u/nobilis_rex_ [link] [comments]  ( 9 min )

Morgan Stanley Research has valued Tesla's soon-to-be-released Dojo supercomputer at up to $500 billion, potentially increasing the auto giant's valuation significantly. The financial institution believes Dojo’s applications will go beyond Tesla's Full-Self Driving (FSD) capabilities. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/yhia3c5f1vnb1.jpg?width=1440&format=pjpg&auto=webp&s=7adf40e7b868a4fbf6eb3d696132652f4f549f23 Morgan Stanley's Bullish Prediction on Dojo Morgan Stanley has suggested that Dojo might not just enhance Tesla's FSD technology, but could find use in other devices that make real-time decisions based on a visual field. Apart from raising Tesla’s valuation, this could potentially open up new markets for the company. Following this, Morgan Stanley has increased its target price for Tesla shares from $250 to $400 each. Dojo Supercomputer Overview Tesla has developed Dojo in-house, diverging from conventional AI accelerators and involving its own computing, networking, IO, and instruction set. At the heart of Dojo is the D1 AI accelerator processor, containing 354 custom CPU cores. Twenty-five D1 chips are combined to create a Dojo training tile, which could expedite Tesla’s move towards earning revenue from vehicle software. Future Plans and Implications Tesla could potentially become an AI-as-a-service provider to automakers in need of FSD capabilities with Dojo. As the development of Dojo continues, Tesla has invested in alternative AI infrastructures, including a cluster of 10,000 of Nvidia's most potent H100 accelerators. (source) P.S. If you like this kind of analysis, I write a free newsletter that explores the latest AI developments. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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Somewhere in the world there's a person who is the smartest. Why stop there? There are ten people who are the smartest. And if they are on the Internet, AI will find them. Perhaps not yet. It probably needs to get smarter. Maybe by Gemini. Or GPT-5. But eventually an AI will analyze all of the content on the Internet, and determine from that data who are the ten most intelligent people on the planet, (whose material is online). Of course if AI can determine the top ten, it can certainly determine the top 100, and the top 1,000, and even the top 100,000. I suppose when that happens there will be a lot more human brain power available to solve our problems. Although by then AI will be solving them far better than we could, haha. But think about it for a minute. There are very smart people out there who don't publish in traditional mass media channels. The geniuses among us who don't fit in so well, and are therefore resigned to the margins, remaining unrecognized. Wouldn't it be great if AI discovered them, and gave them the validation they deserve? Wouldn't it be great to find out who they are so that they can better work on whatever. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

The US sanctions on China's AI and semiconductor industries have failed to limit their growth and development. China is rapidly developing supercomputing capabilities and aims to become the world leader in AI by 2030. Chinese companies like Huawei and SMIC continue to import advanced semiconductor manufacturing equipment and develop their own chips, indicating that the export controls have not been effective. The article explores the trajectory of Chinese domestic semiconductor manufacturing and AI capabilities, as well as the potential impact on companies like Apple, Qualcomm, and MediaTek. It also discusses the potential responses from the US and its allies to counter China's advancements. Source : https://www.semianalysis.com/p/china-ai-and-semiconductors-rise Summarized by Nuse AI submitted by /u/NuseAI [link] [comments]  ( 9 min )

You can install it from the Chrome web store. https://reddit.com/link/16gm8bq/video/yyhx45xjgsnb1/player ​ submitted by /u/MiladMansory [link] [comments]  ( 9 min )

Most of them failed. So this was my prompt: Write 5 sentences that all end with the word 'apple'. It was identical in all models. I only did this exactly once for each one. Here’s the results I got of how many of the 5 sentences ended in “apple”. I let “apples” count as an ending as well even though technically that is a fail. Google palm: 0/5 Falcon 180B: 0/5 Bard: 1/5 Claude 2: 1/5 Gpt 3.5: 2/5 Llama2 70b: 4/5 GPT 4: 5/5 Edit: some examples if you’re curious https://ibb.co/yf19rpb https://ibb.co/rcF1qK8 https://ibb.co/VCQxMwy submitted by /u/jgainit [link] [comments]  ( 9 min )

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Although the torchvision library has contains datasets and model architectures for classification, detection, segmentation, and more, it still needs support for object tracking. This YouTube video takes object detection models from torchvision, and uses them with DeepSORT tracker. submitted by /u/spmallick [link] [comments]  ( 9 min )

AI, with its vast resource needs, is raising concerns over sustainability and environmental impact. Last year, Microsoft's data centers drained over 2,500 Olympic-sized swimming pools worth of water, reflecting a 34% increase from the previous year. Google also reported a 20% water consumption increase over the same period. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/npr6uja0crnb1.png?width=990&format=png&auto=webp&s=b775754b9d42a8129fff2cae675f63c3a291f7bc A deeper look at AI's water footprint The growth of AI and related technologies increases the need for vast server farms, which depend heavily on water for cooling purposes. The spike in water usage can be attributed primarily to AI, as per Shaolei Ren, a researcher at the UC, Riverside, who focuses on AI's environmental impact. For every 5 to 50 prompts submitted to ChatGPT, it consumes about 500 ml of water, according to an upcoming paper from Professor Ren's team. Big Tech and Responsible Water Usage Recognizing their significant water consumption, tech companies like Google have voiced concerns and are exploring ways to mitigate the negative effects. Google has committed to responsible water usage, which includes assessing where and how their water usage might affect surrounding areas. (source) P.S. If you like this kind of analysis, I write a free newsletter that explores the latest AI developments. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Alibaba Adds Smart Assistant and Upgraded Image Search to B2B Platform.[1] Collina Strada has called on AI to help create its spring/summer 2024 collection, unveiled during New York Fashion Week.[2] LexisNexis is embracing generative AI to ease legal writing and research.[3] Snowflake CEO says people will soon not be able to remember a world without AI.[4] Sources: [1] https://www.pymnts.com/commercial-payments/2023/alibaba-adds-smart-assistant-and-upgraded-image-search-to-b2b-platform/ [2] https://www.thenationalnews.com/lifestyle/fashion-beauty/2023/09/11/collina-strada-ai-new-york-fashion-week/ [3] https://techcrunch.com/2023/09/10/lexisnexis-generative-ai/ [4] https://www.cnbc.com/2023/09/11/snowflake-ceo-says-people-will-soon-not-remember-a-world-without-ai.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

​ https://preview.redd.it/l9jkupupaqnb1.jpg?width=1125&format=pjpg&auto=webp&s=ba52e00e32119d34958c480473bfa690484cd085 ​ https://preview.redd.it/w53cjsqqaqnb1.png?width=1125&format=png&auto=webp&s=f750ca0996b94fb530173a8a9c4a2a258e29517a submitted by /u/guh-eye [link] [comments]  ( 8 min )

Hi, all, I'm working on a GPT-powered game where the characters speak using API calls. For this, I need an inexpensive vector database that does not require an API, or at least if it does, it leverages the OpenAI API. Also, this vector database must be runnable on consumer-grade gaming hardware with a small search space (let's say 10-50 entries in the DB). Also, I need to package it with the game somehow. My game is in the Godot engine which can use Python with a plug-in. Other approaches I was considering is having a second process communicate with the game through a socket. Ideally the vector DB solution would be easy to install - that is, I could package it with a .exe, and simply run both without the player having to download anything else. Any suggestions? submitted by /u/kettlebot141 [link] [comments]  ( 9 min )

Hi. I'm currently tasked with something at my company that I'm facing some difficulty with because it's not in my domain. My company has a service where we provide video chatting to users and match users with others based on various features. Currently I've implemented a simple model where we have separate embedding matrices for each user feature, create a user representation by aggregating these features, and performing regression between two users. The way that regression works is that the final score output from the model would act as a "matching score" and we'll match user A with the highest other user. The problem is that obviously running inference on every single pair of users is very slow and I need to speed this up. Some methods I thought about were to either use a feature store or perform sampling on users so we're not running inference on the entire users, but I'm not sure if this is optimal. Just curious what other people who have tackled problems like these have done and looking for second opinions. Thanks. submitted by /u/Seankala [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.05519 Blog: https://next-gpt.github.io/ My opinion: It lacks a Cognitive Architecture: https://arxiv.org/abs/2309.02427 Also the models are far too small and are more on the gpt-2 level. The idea in itself is a good one but can be far improved with bigger models. I also would like to remember in this that all foundation models could be improved if there would be no tokenizers: https://x.com/karpathy/status/1657949234535211009?s=20 Abstract: While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities. As we humans always perceive the world and communicate with people through var…  ( 9 min )

Paper: https://arxiv.org/abs/2309.01660 Abstract: With their recent development, large language models (LLMs) have been found to exhibit a certain level of Theory of Mind (ToM), a complex cognitive capacity that is related to our conscious mind and that allows us to infer another's beliefs and perspective. While human ToM capabilities are believed to derive from the neural activity of a broadly interconnected brain network, including that of dorsal medial prefrontal cortex (dmPFC) neurons, the precise processes underlying LLM's capacity for ToM or their similarities with that of humans remains largely unknown. In this study, we drew inspiration from the dmPFC neurons subserving human ToM and employed a similar methodology to examine whether LLMs exhibit comparable characteristics. Surp…  ( 9 min )

Hey everyone, I'm trying to create an alignment between source audio in a different language than the translated transcript. Essentially want to align the translated transcript with the word-level timestamps on an Audio, programmatically. I've tried to find different tools; some open-source ML models force alignment if the source audio and transcript language are the same. My goal is to have audio in a dubbed language, which I generate using a translated transcript that has been originally transcribed from my audio. Alignment seems tough since languages are spoken at different rates, so I'm figuring out the best way to optimize alignment without having to speed up/slow down the audio too much for each sentence. submitted by /u/Revolutionary_Ant944 [link] [comments]  ( 9 min )

I'm in need of a massive amount of GPUs for batch inference I'm doing. Outside of the big cloud providers are there any niche services out there you'd recommend? submitted by /u/Ok_Post_149 [link] [comments]  ( 9 min )

I gave a talk at work the other day about the attention mechanism and one of my coworkers told me that he thinks Fourier Positional Encodings in transformers are outdated. I've tried to follow up and find what I could but I didn't see anything suggesting that they're not being used. I know that learned encodings are also used. Can anyone give me some direction on this? My initial impression is that they are not outdated by any means, but I'm happy to be wrong about that. submitted by /u/XfrmrTron [link] [comments]  ( 9 min )

I've created an AI Sound service that can do TTS (text to speech), STS (speech to speech), Voice Cloning, generate sound effects, and also generate instrumental music. Here's the link: https://voicegen.org/, you can try everything for free. The TTS quality is similar to Elevenlabs, and there are some sample clips on the home page. Stuff I'm working on: - Emotional speech (where you can select the emotion of the TTS). Right now you can already do it by putting the emotion in brackets: e.g. "[Angrily] Please go away!" but I want to make it better. - Music with vocals. Currently the model only generates instrumental music. I am retraining it/tweaking the model to allow for music with lyrics. - Faster Inference: Since I'm doing this all myself and I'm not rich, I don't have access to the best hardware. However, I am working on some optimizations like speculative decoding that should speed things up. Anyways, let me know if you have any questions/comments/feature suggestions/see any bugs! Feel free to DM me. Thanks. submitted by /u/ginger_turmeric [link] [comments]  ( 9 min )

Hey all, we just released our new project/paper and we thought you all might find it useful! Our project (Kani) is a super lightweight and hackable alternative to frameworks like LangChain or simpleAIchat meant to help developers hook in callable functions or tools to chat models easily. With Kani, devs can write functions in pure python and just add one line (the @ai_function() decorator) to turn any function into an AI-callable function! Kani works with any model and has built-in tools for OpenAI, HuggingFace, LLaMAv2, Vicuna, and GGML with more to come. Kani also never does any prompt engineering under the hood and doesn't require learning complex library tools---all defaults are minimal and highly customizable. Check out our Colab for mini-examples of things like retrieval, web-search, model routing, etc. https://colab.research.google.com/github/zhudotexe/kani/blob/main/examples/colab_examples.ipynb If you're interested in learning more check out our links below! Paper: https://arxiv.org/abs/2309.05542 GitHub: https://github.com/zhudotexe/kani Docs: https://kani.readthedocs.io/ submitted by /u/zhuexe [link] [comments]  ( 9 min )

So i have been doing some research to get into the LLM quantization field but have some questions. To better organize my ideas i have developed the image below. Does it make sense / is true? The way i understand it there are 3 main methods which are compatible with different backends (the backend part is still quite confusing to me). What is the core diference between what the methods do and the backends? What are some core diferences between the backends? What is the main distinction between GPTQ and NF4? How does NF4 relate to QLoRa, is it the same or is it just a small part of QLoRa? Thanks in advance and i apologize for any ignorance. ​ https://preview.redd.it/gxpo0ir0yunb1.png?width=1041&format=png&auto=webp&s=872424a58a9d4393c025b8d2cec0160979b035f4 submitted by /u/MiNeves [link] [comments]  ( 9 min )

Paper - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425828/ Six patients 65 years or older (2 women and 4 men) were included in the analysis. The accuracy of the primary diagnoses made by GPT-4, clinicians, and Isabel DDx Companion was 4 of 6 patients (66.7%), 2 of 6 patients (33.3%), and 0 patients, respectively. If including differential diagnoses, the accuracy was 5 of 6 (83.3%) for GPT-4, 3 of 6 (50.0%) for clinicians, and 2 of 6 (33.3%) for Isabel DDx Companion. ​ submitted by /u/MysteryInc152 [link] [comments]  ( 9 min )

Hello, everyone, I'm currently working on a computer vision project using the Oxford Pets Dataset, which consists of 37 different pet categories. I initially used a pre-trained ViT model with ImageNet weights model=vit_b_32(ViT_B_32_Weights.IMAGENET1K_V1), and it gave me an impressive accuracy of 88%. However, I want to modify the architecture of the ViT model and train it from scratch without relying on ImageNet weights. I'm aware that ViT models are data-hungry and that training from scratch (model=vit_b_32( )) can be challenging, especially with limited data. I've already applied data augmentation techniques to enhance my dataset, but I'm still struggling to achieve satisfactory results. My accuracy is currently only 7%. I'd appreciate any advice or tips from the community on how I can improve the performance of my scratch-trained ViT model. Are there any specific training strategies, hyperparameters, or architectural modifications that I should consider? How can I make the most out of my limited dataset to boost accuracy? Thank you in advance for your help! submitted by /u/NoEntertainment6225 [link] [comments]  ( 9 min )

Hi community~ We are developing a platform similar to mTurk and Prolific and plan to do the first wave of hypothesis testing in the coming weeks. If you have open tasks that require large amounts of human intelligence, please reply to this thread or dm me. We can support your research in our hypothesis testing. we are on the mission of helping machine learning experts and AI training as open and public goods, you can learn more here: https://ivynetwork.cloud/ feel free to ask more questions here :) submitted by /u/Accomplished_Code_25 [link] [comments]  ( 9 min )

İ have a question. How important linear algebra for machine learning? İ have basic level knowledge on linear? Should İ study in more detail? And How can İ follow roadmap on math for machine learning? submitted by /u/Necessary-Car-5080 [link] [comments]  ( 9 min )

Hello people. I've been wanting to clone voices along with the accent. For example: A user speak English in an Indian accent should have that accent cloned in the output audio as well. By default, tortoise is not good at doing that. It can clone the pitch of the voice really well but the accent is completely lost. I was wondering if fine tuning the model could get me what I want. Please do suggest. Also do suggest any methods on fine tuning it if it does in fact help my use case. Thanks a lot! Note: I've also tried bark, coqui and vall-e-x. They aren't that good for voice cloning from what I saw. submitted by /u/salehxoxo [link] [comments]  ( 9 min )

I’ve been working on a simple ML classifier that predicts the size of a piece of clothing based in user body specifications (such as height, weight, age, etc). As we want to move the model to production, I’m keen on understanding the best strategies to evaluate its performance in a real-world setting. This is a small example of how we would “tag” our recommendations: If the model predicts a size M, and the user buys and keeps it, it’s a correct prediction If the user buys and M and returns it due to size issues, it’s incorrect If the user buys a different size, returns it, and then buys the size initially recommended, it’s counted as correct. Additionally since we cache user input data, when they visit the same product after a while, or if they visit different product pages, they don’t need to re-input. We need to determine if they looked at this recommendation or if they even took it into account, especially if some time has passed since the original recommendation was made. Main questions I have: 1. What scenarios might I be missing when tagging incorrect/correct predictions 2. How would you approach the data tagging issue in this context? 3. What would be the best strategy to determine if a recommendation was considered by the user or if it’s too old to be reliable? I’ve gotten some insights already but would love to hear more perspectives. Any feedback, experiences, or even related research would be much appreciated! submitted by /u/SufficientPepper1801 [link] [comments]  ( 9 min )

We are working on a research project on how to identify contextual hidden bias in real use cases, by simulating real-world situations where AI systems behave in unexpected biased ways, regardless of how well their models have performed during development with their training and testing datasets. Would love to hear from those, I'm interested to know if their systems had any issues with misbehaviour in post-market use cases and how they are solving/mitigating this. Ps: If anyone is interested to get involved with the wider research, let me know! submitted by /u/Dismal-Might8594 [link] [comments]  ( 9 min )

Hello r/MachineLearning! After the positive response to Bargainer.ai, I got really excited about the potential of this technology in larger scale video games like World of Warcraft or GTA for example. I'm happy to announce that I'm now releasing - Convince the Bouncer! This time, you chat with an AI Bouncer and try to gain entry to a very exclusive night club. Don't worry; it's fairly easier than getting into Berghain. Try it out here: convincethebouncer.com P.S.: Get the VIP Pass from the Bouncer, and you might access an upcoming AI platform early! :) Questions or ideas? Let me know. Thanks a bunch! submitted by /u/gavo_gavo [link] [comments]  ( 9 min )

Hello, ​ I am an information systems student currently conducting research for my undergraduate thesis on the factors that influence people's adoption intention of ChatGPT, as well as identifying the factors that may be holding them back. These factors include people's concerns about potential negative impacts of ChatGPT, such as increased unemployment and the spread of misinformation. Your participation in this study is crucial as it will provide valuable insights to help us understand how ChatGPT can be improved to meet users' needs. ​ Please note that I am not affiliated with OpenAI, no identifying information will be collected during the survey, and all responses will be kept confidential. The survey should take approximately 10 to 15 minutes to complete, and participation is voluntary. You may withdraw from the survey at any time, and there are no known risks associated with participating. ​ If you are interested in learning more about the study, please follow the link below. ​ https://docs.google.com/forms/d/e/1FAIpQLSf5HIfXHppMuTR63x00i4OuRAtM5Ti6EGybd-HuI1kmK06VPw/viewform?usp=sf_link ​ Thank you for taking the time to contribute to our research study. Your participation is greatly appreciated! submitted by /u/maulanash [link] [comments]  ( 9 min )

I want to train an LLM model on data related to the Container Orchestration Platform Kubernetes. The LLM should be able to identify issues, provide commands, and provide troubleshooting solutions for a given input. What is the best model for doing so and how much data should I have to train the model? submitted by /u/faizanbasher [link] [comments]  ( 9 min )

Arxiv link: Textbooks are all you need II More generally, phi-1.5 (1.3B) exhibits many of the traits of much larger LLMs, both good – such as the ability to "think step by step" or perform some rudimentary in-context learning – and bad, including hallucinations and the potential for toxic and biased generations – encouragingly though, we are seeing improvement on that front thanks to the absence of web data. We open-source phi-1.5 to promote further research on these urgent topics. submitted by /u/PantsuWitch [link] [comments]  ( 9 min )

Posted by Matt Barnes, Software Engineer, Google Research Routing in Google Maps remains one of our most helpful and frequently used features. Determining the best route from A to B requires making complex trade-offs between factors including the estimated time of arrival (ETA), tolls, directness, surface conditions (e.g., paved, unpaved roads), and user preferences, which vary across transportation mode and local geography. Often, the most natural visibility we have into travelers' preferences is by analyzing real-world travel patterns. Learning preferences from observed sequential decision making behavior is a classic application of inverse reinforcement learning (IRL). Given a Markov decision process (MDP) — a formalization of the road network — and a set of demonstration traject…  ( 92 min )

In an event at the White House today, NVIDIA announced support for voluntary commitments that the Biden Administration developed to ensure advanced AI systems are safe, secure and trustworthy. The news came the same day NVIDIA’s chief scientist, Bill Dally, testified before a U.S. Senate subcommittee seeking input on potential legislation covering generative AI. Separately, Read article >  ( 6 min )

Generative AI’s transformative effect on the auto industry took center stage last week at the International Motor Show Germany, known as IAA, in Munich. NVIDIA’s Danny Shapiro, VP of automotive marketing, explained in his IAA keynote how this driving force is accelerating innovation and streamlining processes — from advancing design, engineering and digital-twin deployment for Read article >  ( 7 min )

Ten miles in from Long Island’s Atlantic coast, Shinjae Yoo is revving his engine. The computational scientist and machine learning group lead at the U.S. Department of Energy’s Brookhaven National Laboratory is one of many researchers gearing up to run quantum computing simulations on a supercomputer for the first time, thanks to new software. Yoo’s Read article >  ( 6 min )

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks and demonstrates how NVIDIA Studio technology improves creative workflows.  When it comes to converting 2D concepts into 3D masterpieces, self-taught visual development artist Alex Treviño has confidence in the potential of all Read article >  ( 7 min )

Announcements Top Stories In-Depth The post DSC Weekly 12 September 2023 appeared first on Data Science Central.  ( 20 min )

By Colin Priest, Chief Evangelist at FeatureByte Enterprises are increasingly implementing Artificial Intelligence (AI) into their operations. However, AI-ready data pipeline practices are still in their infancy, especially when it comes to IT security. The pervasiveness of “Spaghetti Code” Enterprises delving into AI data pipelines often find themselves wading through a mess of complex and… Read More »Securing your AI data pipeline with MLOps The post Securing your AI data pipeline with MLOps appeared first on Data Science Central.  ( 20 min )

Businesses today constantly strive to gain a competitive edge in their marketing efforts.  Leveraging their data effectively to create data-driven campaigns is the best way to trump the competition. One of the best tools at their disposal to utilize their data is a data warehouse. Data warehousing is crucial in enhancing marketing and campaign management… Read More »Data Warehousing: The key to effective marketing campaign management The post Data Warehousing: The key to effective marketing campaign management appeared first on Data Science Central.  ( 21 min )

The way we work has changed, with remote teams now a common part of the landscape. While remote work offers flexibility, it also brings challenges. Managing remote teams effectively is crucial to ensure productivity and collaboration. In this article, we’ll explore how using time tracking for remote teams can help manage employees’ performance better. Time-tracking… Read More »Data-driven insights: Improving remote team performance with time-tracking analytics The post Data-driven insights: Improving remote team performance with time-tracking analytics appeared first on Data Science Central.  ( 21 min )

In the panorama of Artificial Intelligence (AI), Natural Language Understanding (NLU) stands as a citadel of computational wizardry. No longer in its nascent stage, NLU has matured into an irreplaceable asset for business intelligence. In this discussion, we delve into the advanced realms of NLU, unraveling its role in semantic comprehension, intent classification, and context-aware… Read More »AI for Natural Language Understanding (NLU) The post AI for Natural Language Understanding (NLU) appeared first on Data Science Central.  ( 21 min )

The Internet of Things (IoT) has been transforming entertainment and has given it new ways of creating, delivering and consuming content. The wide-ranging utility of IoT devices has improved user experience while enhancing the safety and security of users. The media and entertainment (M&E) companies can leverage IoT technology to improve the overall quality of… Read More »How can IoT transform and benefit the entertainment industry? The post How can IoT transform and benefit the entertainment industry? appeared first on Data Science Central.  ( 20 min )

In our increasingly interconnected world, the digital realm has become both a frontier of innovation and a battleground of threats. As technology advances, so do the tactics of malicious actors who seek to exploit vulnerabilities in our digital infrastructure. The rapid evolution of cyber threats calls for a paradigm shift in defense strategies, and that’s… Read More »AI and the cyber challenge: Bridging vulnerabilities in modern defense strategies The post AI and the cyber challenge: Bridging vulnerabilities in modern defense strategies appeared first on Data Science Central.  ( 22 min )

Hi! I am working on the implementation for DQN algorithm for one interesting game. This game is interesting because moves in this game are not affecting state of the game directly, but modify beliefs of other participants of the game and basically allow other agents to deduce role of other players in the game. It's game of "Mafia". Here's are the rules: Mafia Game description: Game is played with 10 players, players are getting roles at random. At the beginning of the game there's 3 players who gets Black cards (1 Don and 2 Mafia) and 7 players get Red cards (6 Citizen card and 1 Sheriff card). One team is playing against each other. Three black players knows each other and red players do not know who is red and who is black. Game is played with phases - "Day" and "Night". Du…  ( 11 min )

Hiii, I have some troubles here. I'm working on a multi-agent setting with three DQN agents each with its observation plus a shared reward. I tried many hyperparameters values, however I got a curve as below. I don't know why there are some sudden drops. Is there anyone could help me please ? https://preview.redd.it/ua30pe963tnb1.png?width=1753&format=png&auto=webp&s=77fcc91cfaf08984a5f03014bdc1bc9b69c2b2a9 submitted by /u/GuavaAgreeable208 [link] [comments]  ( 9 min )

A curated reading list for the adversarial perspective in deep reinforcement learning. https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning submitted by /u/ml_dnn [link] [comments]  ( 9 min )

submitted by /u/PowerfulGeologist373 [link] [comments]  ( 9 min )

Hello all, I have compiled an article including important components that constitute an Artificial Neural Network and the link is here: https://www.enjoyalgorithms.com/blog/components-of-ann It includes the information regarding the components like: Input layer Output layer Hidden layer/s Neurons Connections Fully connected Feed Forward Network Weight Matrix Activation function Loss/Cost function Optimization Algorithm, and finally Parameters https://preview.redd.it/gq57nbbgmrnb1.png?width=1280&format=png&auto=webp&s=dd44bbf8ab1c60acc74933c982b4f86cc5199e06 All these components help in designing Neural Network Architecture to solve any classification and Regression Problem. Please have a read and give your valuable feedback to improve it further. Enjoy Learning! submitted by /u/ravish_kumar_007 [link] [comments]  ( 9 min )

This research paper was presented at the 28th ACM SIGPLAN International Conference on Functional Programming (opens in new tab) (ICFP), a premier forum for discussing design, implementations, principles, and uses of functional programming. Functional programming languages offer a host of advantages, such as ensuring memory safety (opens in new tab) and eliminating arbitrary side effects. […] The post FP2: Fully In-Place Functional Programming provides memory reuse for pure functional programs  appeared first on Microsoft Research.  ( 10 min )

Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default presets in minutes. SageMaker Studio is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing their data to building, […]  ( 6 min )

This post addresses the challenge faced by developers and support teams when application logs are presented in languages other than English, making it difficult for them to debug and provide support. The proposed solution uses Amazon Translate to automatically translate non-English logs in CloudWatch, and provides step-by-step guidance on deploying the solution in your environment.  ( 6 min )

In this post, we share how SageMaker facilitates the data science team at Scalable to manage the lifecycle of a data science project efficiently, namely the email classifier project. The lifecycle starts with the initial phase of data analysis and exploration with SageMaker Studio; moves on to model experimentation and deployment with SageMaker training, inference, and Hugging Face DLCs; and completes with a training pipeline with SageMaker Pipelines integrated with other AWS services  ( 10 min )

The system could improve image quality in video streaming or help autonomous vehicles identify road hazards in real-time.  ( 10 min )

I have been googling for a few hours now, and all of the solution that I can find are either very complex or not up to date. Ideally I would like to combine this with OpenAI Whisper to clarify the speech audio file and then transcribe it. Any good methods for this? submitted by /u/aszx789 [link] [comments]  ( 9 min )

Hi everyone, "Hallucinations" seem to be one of the major blockers to the adoption of LLMs, especially in enterprise settings where seemingly plausible but false information/decisions can be disastrous. I am wondering whether or not current LLMs, like GPT4 or Llama 2 70b have reached the reasoning capabilities to be able not to hallucinate when fed the proper information. One can see a LLMs as college / high school students that have some basic reasoning and knowledge but might need to be nudged to answer business / scientific questions. What is your opinion on the topic? Is there for instance, a database of hallucinations, so that one could test that if the model would have answered properly if the right context was fed in the prompt? View Poll submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

I've consulted all the AIs and all of the search engines I could think of and am still coming up short. I'm pretty sure I just didn't make this up, somewhere between 2005 and 2015 there was a paper where the authors describe a method for using silicon neuron forests to predict traffic patterns. They etched silicon columns onto a wafer and then submerged the wafer in an ionic solution. They sent electrical signals into the column array and recorded the output. As the signal was allowed to interact with the silicon in solution, the columns would begin to form small conductive tendrils or filaments to neighboring pillars. These filaments formed a neural network, which could be used to predict traffic patterns. I'm sure there were at least 2 papers that came out about this subject, but for the life of me I cannot find them. I've spent many many hours looking for at least one of the papers and am hoping for a longshot...that some kind internet stranger comes across this and can point me in the right direction. submitted by /u/Inevitable-Start-653 [link] [comments]  ( 9 min )

What is the best CNN or method for creating a program that can detect video glitches, artifacts, anamolies and highlight them in an exported video as well as producing a .txt file. I'm currently using YOLOV8 which works a bit but it's over detecting and it may not be scalable. Not sure if it has to do with the size of the dataset. Right now, I'm gathering my own dataset, but would love to know if there is one that already exists. Looking for all and any recommendations. Thank you. submitted by /u/icetyche [link] [comments]  ( 9 min )

Please let me know if there is a better sub for this! I trained a local voice model of my father, who recently passed away. He was a musician, and I found a handful of songs he had recorded in the 80's. Unfortunately but I only have a copy coming from an audio cassette, which I've digitized. There may be a copy on open reel somewhere in our basement but I haven't found it so far. Any, because dad was always writing and recording new songs, my training dataset consists of all original, high quality vocal stems at 96khz/24bit, spanning at least 20 years and consisting of about 30 ish minutes of audio. I also tried starting over but with a speech he gave as additional training, coming out to around 55 minutes of audio. I wanted to use his voice model to essentially remaster his original vocals from the cassette audio, and rerecord the rest of the instruments myself. I isolated the vocals using UVR (also tried mdx23), and tried to clean it up further as much as possible. The RVC vocals comes out alright, but due to the low quality of the audio, it seems to mess up certain words, for example "free" comes out as "fee". The SVC vocals came out with a closer tonality, but had many more artifacts, and pitch had a tendency to go up/down 2 octaves at the end of certain phrases. When using a higher quality sample (like one of my own vocal tracks), these issues aren't so prevalent, although rvc seems to create more of a hybrid sounding timbre than svc, blending the original voice + model rather than just the sound of the model. Happy to post samples of input/output audio, let me know! tl;dr high quality training data, low quality inference audio. Voice model is the same voice as input audio, 1. tips for making the best of what I've got? 2. any way to use text along with input audio and maintain original vibrato? 3. any way to train a UVR/MDX model using a particular voice in order to better isolate that person? thanks :) submitted by /u/bbmaster123 [link] [comments]  ( 10 min )

Paper: https://arxiv.org/abs/2309.02427 Github: https://github.com/ysymyth/awesome-language-agents Twitter: https://twitter.com/ShunyuYao12/status/1699396834983362690 Abstract: Recent efforts have incorporated large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning. However, these efforts have largely been piecemeal, lacking a systematic framework for constructing a fully-fledged language agent. To address this challenge, we draw on the rich history of agent design in symbolic artificial intelligence to develop a blueprint for a new wave of cognitive language agents. We first show that LLMs have many of the same properties as production systems, and recent efforts to improve their grounding or reasoning mirror the development of cognitive architectures built around production systems. We then propose Cognitive Architectures for Language Agents (CoALA), a conceptual framework to systematize diverse methods for LLM-based reasoning, grounding, learning, and decision making as instantiations of language agents in the framework. Finally, we use the CoALA framework to highlight gaps and propose actionable directions toward more capable language agents in the future. https://preview.redd.it/09kdff4sdonb1.jpg?width=1276&format=pjpg&auto=webp&s=7aaa6d59d602f7e9ab124c812bbfa6bba5b7373b https://preview.redd.it/6cly0e4sdonb1.jpg?width=1277&format=pjpg&auto=webp&s=5a5164b84af5f828a668560acb64e5c579693d1f https://preview.redd.it/mvatjf4sdonb1.jpg?width=1277&format=pjpg&auto=webp&s=7c3a039db3a7e0f3de38f761f8aefa1c8d331ae5 https://preview.redd.it/bj5wdj4sdonb1.jpg?width=1270&format=pjpg&auto=webp&s=473b273ae0097aaa51d6578e9c5e3b9c953cc421 https://preview.redd.it/501cnf4sdonb1.jpg?width=1578&format=pjpg&auto=webp&s=c8b1762ac28c89f8ac2f8d4fb6d9ecae06491c3e ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

For learning ann, cnn I used Google colab. But now for learning rnn I have decided to use ide(a fan of jetbrains). But even if I change one line of code who code recompiles. This wasn't case for colab. Is there a feature in pycharm I don't know(I learned python through text so don't know much about ide). I am a newbie. submitted by /u/Coc_Alexander [link] [comments]  ( 9 min )

I would be interested in hearing opinions for using AI for regulatory assurance and compliance in regulated industries, what are your thoughts? Explanation: An AI-driven compliance system ensuring adherence to evolving regulations, minimizing risks, and enabling businesses to operate confidently within legal boundaries. Pairing Large Language Models (LLMs) with blockchain technology to offer a range of benefits, particularly in the context of regulatory compliance. LLMs, powered by advanced natural language processing and machine learning capabilities, can enhance regulatory compliance processes in several ways. Firstly, they can automate the analysis of regulatory documents, helping businesses stay updated with evolving compliance requirements. LLMs can also assist in generating compliance reports, simplifying complex legal language into understandable summaries. Furthermore, by integrating LLMs into smart contracts, businesses can ensure that contract terms adhere to regulatory guidelines automatically. The integration of LLMs with blockchain can significantly improve regulatory compliance by automating document analysis, simplifying legal language, monitoring compliance in real-time, and enhancing customer interactions—all contributing to greater efficiency and accuracy in adhering to regulatory standards. I have a whole technical whitepaper with this stuff on hand, if anyone would like to review it let me know.. submitted by /u/cryptobooty_ [link] [comments]  ( 9 min )

Hey folks, we explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community! Here are links to the demo and blog post submitted by /u/shayanjm [link] [comments]  ( 9 min )

I am currently trying to learn about how autodiff is used in gradient calculations. In the all sources I've came across, none can explicitly point to an authoritative, consistent or complete source on autodiff. I don't need examples of autodiff, I just need the full, generalized algorithm laid out. For example: I open this link: https://w3.cs.jmu.edu/spragunr/CS445/lectures/autodiff/autodiff.pdf which simply provides autodiff applied on an example, but not the description of the algorithm. The author of that link says if I need any more info, I should go to this other link: https://stats.stackexchange.com/questions/224140/step-by-step-example-of-reverse-mode-automatic-differentiation/235758#235758 But this link doesn't even have topological sort as part of the operation. Therefore these descriptions of the autodiff is inconsistent and leaves me not knowing who to trust. Can someone point to some original paper on autodiff or a single source that describes this algorithm fully? I note here that this situation is completely different from backpropagation. The full backpropagation algorithm is impeccably laid out in peer-reviewed text books such as Learning from Data by Mustafa et al. and Optimization textbook by Chong and Zak. Furthermore, the algorithm defined in these two books are completely consistent with one another. submitted by /u/fromnighttilldawn [link] [comments]  ( 9 min )

https://preview.redd.it/14fj73aw2nnb1.jpg?width=1200&format=pjpg&auto=webp&s=e94612b3dec5f7b9f71490ad5a60ced73fb3373d Researchers at MIT and the Dana-Farber Cancer Institute have developed an innovative AI-powered model called OncoNPC, designed to assist in identifying the origin of tumors in cancer patients where the primary site is unknown. For a small percentage of cancer patients, pinpointing the origin of their cancer can be incredibly challenging, making it difficult to select the most appropriate treatment, as many cancer drugs are designed for specific cancer types. Using machine learning, the researchers created OncoNPC, a computational model capable of analyzing the genetic sequences of approximately 400 genes. This model, based on genetic data routinely collected at Dana-Farbe…  ( 11 min )

Hello, I want to start a small experiment in my company and install an AI locally on my computer, extra only locally, so no company data can be stolen. The AI should be able to summarize large PDFs. Which textmodel can you recommend me, which is local and not too big? submitted by /u/DesNutella [link] [comments]  ( 9 min )

Hi all, So, I normally work in a different field (macroeconomics/econometrics) and I have been working on a slightly different project for a bit because a change of pace is fun every once in a while. The problem is I am running out of good ideas on where to continue and i would appreciate any input! The problem set up is as follows: I am building a second draft for a forecasting toolbox based on a custom ARIMA framework. The problem with this type of exercise is always model selection. For this project I focus on in-sample criterions as the data has very few time periods overall. The typical strategy to find a decent model is to make an initial guess (i.e., a constant model, a random walk model, etc.) and then run a stepping algorithm that probes the model space and, ideally, it coverg…  ( 10 min )

Hey Redditors, We're thrilled to announce a new feature from Qwak.ai—Vector Store—and we're on the hunt for beta testers to help us make it even better. 🔍 What's Vector Store? Vector Store is a next-level solution for managing vector data. It's designed to empower organizations to harness the power of vector search on their own datasets. Here's what it offers: 🔄 Automated Data Ingestion: Schedule jobs to pull data from databases like Snowflake, BigQuery, and RedShift. 🎯 Easy Vector Search: Use our Python SDK or REST API to search, upsert, or delete vectors effortlessly. 🛡️ Secure Storage: Your vectors are stored securely and are always accessible when you need them. 📖 Learn More About Vector Store 🤝 Why We Need Beta Testers We're keen to gather insights on usability, performance, and any bugs that might pop up. 📝 How to Get Involved Interested in being a part of this? Comment below or shoot us a DM. submitted by /u/Practical-Lecture733 [link] [comments]  ( 9 min )

Hi. I need to segment some short strings, they are about 100 tokens in length (BERT tokens), or 20-40 words. 1 segment per text. I'm currently using Spacy first, and if it fails, then use BERT. It's alright, but BERT is not really up to par to what I hope for. I'm wondering if there is some better use, as this is my first NLP project. I've trained the huggingface BertForTokenClassification to label the text as either part of segment, or not part. So binary token classification, then smooth out outliers and pick the longest segment of each text. Each string has only 1 segment. I've trained the BERT with 500 examples. I can easily make more train data, though training on my laptop takes time. If there are better/alternative approaches, I'd love to hear them. Regex rules etc. don't apply, which is why I'm using DL models. Especially I feel like I should segment the whole segment at a time, not by token. submitted by /u/Infamous-Bank-7739 [link] [comments]  ( 9 min )

I want to take a second to express my appreciation for the Folktables datasets! Folktables is a Python package that contains datasets derived from US Census data. The datasets cover topics about income, employment, health, transportation, and housing. They are quite useful for studying the effects of distribution shifts on ML models. For example, one could design experiments to: 1. Study the model's performance under geographic distribution shifts: Each prediction problem in Folktables can be instantiated with data from every US state. So one could use Folktables to study questions around geographic distribution shifts. For example, we can train a classifier using data from California and then evaluate it on data from Michigan. 2. Study the model's performance under temporal distribution shifts. Folktables contains data for several years, which in itself constitutes a form of temporal distribution shift. So, we can train a classifier using employment data from California in 2014 and evaluate how its equality of opportunity violation or accuracy varies over time. Finding non-synthetic (and open-access) datasets that exhibit these behaviors is so hard! Yet, it's quite easy to encounter them in production environments. 🫠 So, big kudos to the UC Berkeley and Toyota Research Institute research teams for crafting these datasets. Folktables Python package: https://github.com/socialfoundations/folktables Link to paper where Folktables was introduced: https://arxiv.org/pdf/2108.04884.pdf submitted by /u/santiviquez [link] [comments]  ( 9 min )

Hey, I'm working on Attitude-Heading Reference System, and I would like to make it with EKF. My approach is to use Euler angles for computing [roll, pitch, yaw] matrix. I read a book "Small unmanned aircraft: theory and practice" and watched few videos on YouTube that are reffering to this particular book. But there is a thing in correction step that I do not fully understand. To be clear, the correction looks as follows: x = x_(t-1) + K*(z - h(x)) Both in the book and in videos, the state vector "x" is [roll, pitch]. But "z" and "h(x)" are the actual and predicted accelerometer readings [ax, ay, az]. So it looks to me, that they try to correct prediction of angles with readings in acceleration: [roll, pitch] = [roll, pitch]_(t-1) + K*[ax, ay, az]. What am I missing? submitted by /u/Skrz_ [link] [comments]  ( 9 min )

We recently benchmarked whisper-large-v2 against the substantial English CommonVoice dataset on a distributed cloud (SaladCloud) with consumer GPUs. The Result: Transcribed 137 days of audio in 15 hrs for just $117. Traditionally, utilizing a managed service like AWS Transcribe would set you back about $10,500 for transcribing the entirety of the English CommonVoice dataset. Using a custom model? That’s an even steeper $13,134. In contrast, our approach using Whisper on a distributed cloud cost just $117, achieving the same result. The Architecture: Our simple batch processing framework comprises: Storage: Audio files stored in AWS S3. Queue System: Jobs queued via AWS SQS, with unique identifiers and accessible URLs for each audio clip. Transcription & Storage: Post transcript…  ( 10 min )

Hello, I wrote CNN using tensor flow to predict phenotypes (cognitive impairment (0/1) and language ability (0-3)) from .vcf files. I transformed the .vcf into tabular format and after determining which columns to keep, I have Chromosome Position Mutation (taken from ref/alt columns) Genotype And merged it with the phenotype data so basically every row has the cog and language scores. I feel like this is a bad way to go about doing this since the model is likely predicting cog/language scores for each mutation rather than each patient as a whole. Am I wrong? How can I fix this so it’s more of a composite of all mutations per subject that the model trains on? Thanks! submitted by /u/Pristine_Ingenuity49 [link] [comments]  ( 9 min )

Given a video with audio, we can use ASR to get a script of the sentences and timestamps. We are looking for a way to group the sentences into chapters. There are several companies that are doing it nicely - Google on YouTube, Assembly AI, but we couldn't find any good resource or paper that explains the research behind how they do it. BertTopic seems to give us just a topic for each sentence, but not a way to split the video. it also doesnt account for timestamps. Wondered if anyone has any links or any other ideas? Thanks very much! Lior submitted by /u/liormessinger [link] [comments]  ( 9 min )

Hi everyone I've been working for quite some time on this project and any feedback will be greatly appreciated. Basically, I've been testing different data structures for large sequence prediction and clustering. Mainly SARSCov2 viral sequences due to its availability. At the moment, I have published two preprints https://www.researchsquare.com/article/rs-2797280/v3 https://www.researchsquare.com/article/rs-1691291/v1 and a general summary of the findings can be found here. https://github.com/TavoGLC/SARSCov2Solar https://www.kaggle.com/code/tavoglc/a-computational-description-of-sarscov2-adaptation I've tried to publish it a couple of times with no success and no comments regarding its accuracy or any potential problems. I hope you guys can check it out and provide some feedback if possible. Just for full transparency, I'm trying to raise funds to further develop those techniques. Donations are extremely welcomed but not encouraged at the moment, just disclosed for transparency. submitted by /u/TavoGLC [link] [comments]  ( 9 min )

I work with device logs that are massive text files, filled with data that's hard to go through manually. I'm primarily interested in extracting specific events or insights, such as security incidents or device malfunctions. The conventional method would be to use regular expressions to filter out relevant information since the logs are structured, but I'm curious about leveraging Large Language Models for this task. I've experimented a bit with zero-shot learning for text summarization but didn't get satisfactory results. Before I invest more time into fine-tuning an LLM, I'd love to hear from anyone who has experience or advice on how to approach this problem. Could LLMs potentially make the process more efficient and effective? Any pointers or suggestions would be greatly appreciated. submitted by /u/Practical_Mango_8720 [link] [comments]  ( 9 min )

Hello, fellow AI enthusiasts! After the positive response to Bargainer.ai, I got really excited about the potential of this technology in larger scale video games like World of Warcraft or GTA for example. I'm happy to announce that I'm now releasing - Convince the Bouncer! Chat with an AI Bouncer and try to gain entry to Elysium, the most elite night club. Don't worry; it's fairly easier than getting into Berghain. Give it a spin here: convincethebouncer.com P.S.: Get the VIP Pass from the Bouncer, and you might access an upcoming AI platform early! :) Questions or ideas? Let me know. Thanks a bunch! submitted by /u/gavo_gavo [link] [comments]  ( 9 min )

Any good recommendations for a vote bot to cast votes for a insignificant online poll? It’s a no security website. Just refresh and hit a vote button. submitted by /u/fa6664 [link] [comments]  ( 9 min )

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I check every now and then. I use some good ones that can summarize a short YouTube video, or a brief-ish article. But I really want something that can chomp down a whole book and distill its essence without making "summaries of summaries", which tend to increase inaccuracy and errors. A good summary is concise and precise, and I want flexibility with bullet points and level of detail. Having issues with ChatGPT-based tools' token limits, and some that purport to support GPT4 (I could use that "advanced reasoning") but but have to fall back to GPT3 for various errors and reasons. So I'm open to Claude-based ones (may be too early, mo' tokens) and other proprietary engines. What is everyone using, including paid (rightfully so if they offer value for the money) services? submitted by /u/Torley_ [link] [comments]  ( 9 min )

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Meta is working on a new AI model to rival GPT-4. The company is acquiring AI training chips and building data centers to create a powerful chatbot. CEO Mark Zuckerberg wants it to be free for companies to create AI tools with. Meta is assembling a group to build the model and speed up the creation of AI tools that can emulate human expressions. There are rumors of generative AI features and the launch of AI 'personas' this month. Source : theverge.com submitted by /u/NuseAI [link] [comments]  ( 9 min )

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I’m a Business generalist who worked with multiple tech led businesses and looking to understand fundamentals of the technology from scratch. Please help me with any relevant courses/reading material/YT channels etc. that can help me kickstart the journey. P.S. I have a brief background with Business Analytics but haven’t done any coding extensive ever in my life. Thanks in Advance submitted by /u/Firm_Brother_7124 [link] [comments]  ( 9 min )

Chinese big tech Tencent has announced a medical large-scale model and unveiled a brand new matrix of AI products for various scenarios, including intelligent Q&A, a family doctor assistant, and a digital medical imaging platform. The drug discovery platform “Yunshen” (iDrug) has also revealed a new protein structure prediction algorithm framework called “tFold.”[1] Morgan Stanley is gearing up to implement an artificial intelligence-driven chatbot, a strategic move aimed at delivering valuable insights and administrative support to their team of financial advisors.[2] A fresh Russian AI bot has displayed larger potential than the most famous chatbot created by US-based OpenAI, IT giant Yandex alleged in remarks to RIA Novosti on Saturday.[3] Meta is developing a new, more powerful AI system, Wall Street Journal reports.[4] Sources: [1] https://drug.ai.tencent.com/en [2] https://voonze.com/morgan-stanley-introduces-ai-powered-chatbot-for-enhanced-services/ [3] https://menafn.com/1107040379/Russian-AI-bot-shows-larger-potential-than-ChatGPT [4] https://www.reuters.com/technology/meta-is-developing-new-more-powerful-ai-system-wsj-2023-09-10/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Citing GPT-4 as the benchmark, Meta is reportedly gearing up to train a new, highly sophisticated AI model. The company is investing heavily in AI training chips and boosting its data centers to support the ambitious project. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/ts4a6reeuinb1.jpg?width=1440&format=pjpg&auto=webp&s=940be18b0c5f13e2762e1664a96274b314fe00df Meta’s vision for its new AI model Meta's aim is to create a powerful chatbot in line with OpenAI’s GPT-4 capabilities. The company has reportedly been in pursuit of Nvidia H100 AI-training chips and is amplifying its infrastructure. The idea is to independently train its new model without outsourcing to platforms like Microsoft's Azure. Efforts and roadblocks Meta plans to begin the training of this LLM early in 2024, emphasizing free access for companies. Despite its grand vision, Meta has encountered obstacles like researcher attrition and contentious resource allocation amidst multiple LLM projects. Noteworthy is the intense competition from major players such as Apple, Google, and Amazon to integrate widely generative AI in their user interface. Broader implications While OpenAI has not immediately revealed plans for a GPT-5, other tech giants are investing heavily. Apple's investment in its "Ajax" AI model signifies the increasing race to advanced AI. This move by Meta represents the ongoing trend of tech conglomerates expanding in the AI space, a fact revealed through Google and Microsoft’s use of AI in their productivity tools and Amazon's ongoing developments. (source) P.S. If you like this kind of analysis, I write a free newsletter that explores the latest AI developments. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Luiza GPT Assistant is a simple virtual assistant that mimics your friend, girlfriend or boyfriend, based on neural network ChatGPT and Telegram. Get unique good morning wishes, goodnight, compliments or just chat. https://github.com/r57zone/LuizaGPTAssistant submitted by /u/r57zone [link] [comments]  ( 9 min )

Dimension 2 The equation for the perimeter of an ellipse is where a is the semimajor axis, e is eccentricity, and E is a special function. The equation is simple, in the sense that it has few terms, but it is not elementary, because it depends on an advanced function, the complete elliptic integral of the […] Hyperellipsoid surface area first appeared on John D. Cook.  ( 6 min )

I posted some notes this morning on how to find the perimeter of an ellipse given its axes. The notes include a simple approximation, a better but more complicated approximation, and the exact value. So given the semi axes a and b, the notes give three ways to compute the perimeter p. If you are […] Solve for ellipse axes given perimeter first appeared on John D. Cook.  ( 5 min )

The home team lost in a new way yesterday. The Baltimore Ravens beat the Houston Texans by 25-9. This was the first time that score has been seen in the NFL. Possible individual team scores How many scores are possible? It is possible to score any number of points except 1. You can score 2 […] Possible and actual football scores first appeared on John D. Cook.  ( 5 min )

Today, we are excited to announce that the Falcon 180B foundation model developed by Technology Innovation Institute (TII) is available for customers through Amazon SageMaker JumpStart to deploy with one-click for running inference. With a 180-billion-parameter size and trained on a massive 3.5-trillion-token dataset, Falcon 180B is the largest and one of the most performant models with openly accessible weights. You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. In this post, we walk through how to discover and deploy the Falcon 180B model via SageMaker JumpStart.  ( 14 min )

Amazon SageMaker Domain supports SageMaker machine learning (ML) environments, including SageMaker Studio and SageMaker Canvas. SageMaker Studio is a fully integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models, improving […]  ( 10 min )

I want to log information like this: ------------------------------------ | rollout/ | | | ep_len_mean | 48.1 | | ep_rew_mean | 2.71 | | time/ | | | fps | 452 | | iterations | 1000 | | time_elapsed | 11 | | total_timesteps | 5000 | | train/ | | | entropy_loss | -0.67 | | explained_variance | -32 | | learning_rate | 0.0007 | | n_updates | 999 | | policy_loss | -0.0567 | | value_loss | 0.0158 | ------------------------------------ to an excel file. Here is my main code: gymnasium.env = snakeEnv() # data_manager = snakeEnv.get_data_manager() # Create an A2C model model = A2C("MlpPolicy", gymnasium.env, verbose=1, device="cuda") # Train the model (replace 'total_timesteps' with appropriate values) model.learn(total_timesteps=100_000, log_interval=1000) Here is the relevant code in my agent file: class snakeEnv(gym.Env): def __init__(self): super(snakeEnv, self).__init__() # Define action and observation space # They must be gym.spaces objects # Example when using discrete actions: self.initNames() self.action_space = spaces.Discrete(3) self.gameCount = 0 self.record = 0 self.reward = 0 self.score = 0 self.game = SnakeGameAI(self.selectedChallenge) self.observation_space = spaces.Box(low=-1000, high=1000, shape=(11,), dtype=np.uint8) def step(self, action): self.reward, self.done, self.score = self.game.play_step(action) self.observation = self.getState(self.game) self.info = {} return self.observation, self.reward, self.done, self.info def reset(self): self.gameCount += 1 self.data_manager.logData(self.gameCount, self.score, self.record, self.reward, self.game.getDeathReason(),self.game.getHeadPos()) self.game.reset() observation = self.getState(self.game) if self.score > self.record: self.record = self.score return observation It would be nice to be able to log the data in the reset function. I know how work with xslx files, the main things is just being able to get the model data. submitted by /u/MrHank2 [link] [comments]  ( 9 min )

Hello everyone! I want to develop a DRL agent to play a turn-based 1v1 game and I'm starting to plan how to handle things in the future. One potential problem that I thought of is that there is a possible mid turn one-sided decision. An abstraction of the game would be like: There are two players: player A and player B. At the start of each turn, each player chooses an action between 3 possible actions. If player A chose a specific action (let's say action 1), the game asks player B to make a decision (let's say block or not block) and vice versa. Actions are calculated. Next turn starts. What would be a good approach to handle that? I thought of two possible solutions: 1. Anticipate the possibility of that mid turn decision beforehand adding a new dimension to the actions space (e.g. take action 3; if opponent takes action 1, block). That sounds that it could create credit assignment problems e.g. giving credit to the second action when it actually didn't happen. 2. Make two policies with shared value functions. That sounds complicated and I saw that previous works like DeepNash actually did that, but I don't know what problems could arise from that. Opinions/suggestions? Thanks! submitted by /u/victorsevero [link] [comments]  ( 9 min )

Newbie here. How does an continuous action space look like? E.g. The agent should choose an integer between 1 and 10. With discrete action space it could look something like this: def step(self, action): if action == 0: self.chosenNumber = 1 ... if action == 9: self.chosenNumber = 10 how would this look like with an continuous action space? submitted by /u/ChampionshipWhole467 [link] [comments]  ( 9 min )

In its debut on the MLPerf industry benchmarks, the NVIDIA GH200 Grace Hopper Superchip ran all data center inference tests, extending the leading performance of NVIDIA H100 Tensor Core GPUs. The overall results showed the exceptional performance and versatility of the NVIDIA AI platform from the cloud to the network’s edge. Separately, NVIDIA announced inference Read article >  ( 7 min )

“Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.  ( 9 min )

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Hey Reddit, I'm working on a tool to pull data from highly irregular Excel files. I've gotten reasonable results which is extremely fast with standard Python coding, but it's far from perfect due to the lack of standardized templates. Interestingly, when I tested ChatGPT-4 on a sample table, it did a decent job at data extraction. However, relying solely on GPT-4 has its downsides like token limits and slow processing speed (and data privacy issues). Plus, splitting the Excel sheet to fit within these limits results in loss of context and data. I'm considering fine-tuning a language model to post-process data that was in a Pandas DataFrame (perhaps converted to JSON). Has anyone had success with this approach or have alternative recommendations? I've tried Langchain, but it wasn't helpful. I have figured out to extract the relevant columns, but the post-processing part is where I am considering using an LLM which understands the domain and what needs to be extracted based on the examples I feed it. Looking forward to your thoughts! And would be happy to answer any additional questions. submitted by /u/rs35plus1 [link] [comments]  ( 9 min )

I've been using Tensorflow since 2017. I know it wasn't ideal or easy back then, but as an early adopter, I became very proficient with it and it has improved a lot since then. I have developed and deployed many custom models in low-level TF, both with and without utilizing the Keras abstractions. I am very comfortable with it in general. But I'm noticing now that Pytorch is gaining more popularity, all the younger practitioners, who got into deep learning within the last 3-5 years, are Pytorch adopters. I've also heard rumors that even googlers are also abandoning TF. I started playing around with Pytorch and as a TF expert, I couldn't help but getting annoyed at how far it is lagging behind in many abstractions and optimizations. I know things are getting better now with the Pytorch 2.0 and introducing some optimization such as the "compile" functionality, but still many of the pytorch project tools remain in beta such as Torchtext and I find many things very annoying, such as having to set the device and pass it on to layers if you want GPU acceleration, having to install Torchtext and other processing libraries separately, or having to use a Dataloader and the limited data type supports for torchdataset. Most people who have not mastered Tensorflow would not relate to my annoyance. Anyhow I'd really prefer to stay within my comfort zone and continue to develop and improve in TF, but if TF is dying, then I better not to, right? So should I convert? Is it indeed dying? submitted by /u/DieselZRebel [link] [comments]  ( 9 min )

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What do you think of my recent work? https://github.com/jacobmcasey/bayesian_net At its core, its a neural network for Bernoulli naive Bayes. It uses a 3-layer neural network in NumPy for predicting priors of Bernoulli Naive Bayes Would love to get your feedback on this classifier project! submitted by /u/Ok_Grape_3670 [link] [comments]  ( 9 min )

It happens on and off and I have tweaked many hyperparameters but nothing seems to work significantly better. Is there a recommendation on what to check/tweak? submitted by /u/MrAaronW [link] [comments]  ( 9 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 9 min )

Hey, I would like to listen to my PDFs I got. Week would the best / easiest way to get an mp3 from my pdfs with good voice? I got a rtx 3070 to run it locally. submitted by /u/Independent_Hyena495 [link] [comments]  ( 9 min )

For transformer inference, is it ok to pipeline blocks/layers to multiple low-memory GPUs (lower blocks to GPU-A and upper blocks to GPU-B) ? A to B bandwidth should be relatively low, and each GPU needs half the model memory. This increases inference latency - fine for our use case... Not sure how this would affect the KV cache ? The excellent Lil'Log article suggest I read the training optimization article, which has this image which is for training but not for inference submitted by /u/yazriel0 [link] [comments]  ( 9 min )

I am in the process of setting up the first iteration of model monitoring in production. Models are currently served through torchserve in kubernetes (on GCP). In the pasts few years I've been mainly using Vertex AI off-the-shelf tools. Ideally, I'd like to store the following data: Request input. Model prediction. User feedback/groud truth (this might come at a later time). Anything that I might be missing out. This would enable me to implement a wide array (either using libraries like whylogs or evidently that have a lot of the reporting side of things already baked in) of cheks for drift and model degradation. I am wondering what would be the best set up to achieve this. This is currently what I came up with: Save a payload at inference time containing all the fields descr…  ( 10 min )

I want to start using mixed precision in my training, particularly for CV with high-resolution images. HF accelerate seems quite popular nowadays and looks nice. However, in the past I've invested in learning things like Pytorch-Lightning which look good in a minimal example, but actually add more annoyance than they're worth. Pytorch also can do these things, and the boiler plate doesn't look worse at a glance: https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/ https://huggingface.co/docs/accelerate/index ​ Any experiences with either? Cheers! ​ submitted by /u/AuspiciousApple [link] [comments]  ( 9 min )

Hi. Is this really better practice than having human linguists build evaluation sets for domain-specific data? submitted by /u/throwaway34334534 [link] [comments]  ( 9 min )

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If you're working on NLP and you're serious about understanding your data, check out Wordview. Wordview is an open-source Python library designed to make Exploratory Data Analysis (EDA) for text for modern NLP applications simpler and more efficient. It consolidates various analysis tools under one roof —think document metrics, multi-word expressions, language identification, POS tags, and even bias analysis. We're looking for contributors to help us expand and refine its capabilities. Join us in making NLP data analysis easier and more insightful with Wordview! Here is the link to the GitHub page: https://github.com/meghdadFar/wordview If you just want to use Wordview without contributing, you're very welcome too. Note that it's pretty new and we are still testing things. Please hence feel free to report bugs and send us your feedback and opinions. Looking forward! submitted by /u/SyntaxTreeHugger [link] [comments]  ( 9 min )

Hi, I'm an undergrad student working on a machine learning project about motor imagery classification for BCI. One requirement for the project is that I need to implement LIME an explainable AI tool on my model. I ran into a problem rather quickly as my model accuracy quickly decreases as I try and add more subjects to the training data, I'm the publicly avaliable EEG Motor Movement/Imagery Dataset from physio.net. The features I'm currently using for training my model are SVD entropy, Spectral Entropy, Hjorth mobility , Hjorth complexity and CSP components after applying overlapping filter bank. One method I've been suggested is to train models seperately for each subject, I don't know how to go about this should I construct a new model for each subject or should I keep the architecture but reset the training weight, also would this defeat the purpose of implementing LIME in the first place. I'm wondering if there's a way to deal with inter-subject variability without having to make/ train models for each subject seperately. I'm sorry if the choice of features doesn't make much sense since I'm basically self-taught and I have no prior knowledge about EEG or BCI TLDR; how do I deal with inter-subject variability without having to make/ train models for each subject seperately. submitted by /u/Necrozx13 [link] [comments]  ( 9 min )

Hey friends, I have developed a library, called 100gecs, that makes hyperparameter tuning on LightGBM and CatBoost models trivially easy. Background LightGBM and CatBoost are gradient boosted tree models, like XGBoost, and in many cases the best baseline model in supervised learning tasks on tabular data. They work by iteratively fitting trees on data, with each subsequent tree "correcting" on some level the prediction of the prior tree. Here's a good intro, if you want some more background on these methods. Hyperparameter tuning promises an optimally or near-optimally configured model, thus enabling you to get the best baseline model you possibly can. Summary 100gecs provides custom child classes of LGBMClassifier, LGBMRegressor, CatBoostRegressor and CatBoostClassifier that can be …  ( 10 min )

In your informed opinion, how would an archetypical career in ML look like? Looking at job postings, at least in Europe, it looks like the minimum required experience is around 3 years. There seems to be a good demand for mid-senior level positions, but a void for entry level. As I understand it, most DS departments are not big enough to spare resources for newbies that must be trained, the need is for a few but seasoned engineers. How far is my guess from the truth? And, most importantly, how could new candidates (let's say recent MSc in Data Science, for instance) get into the industry? Through analyst/DE roles? submitted by /u/madway99 [link] [comments]  ( 9 min )

Hello everyone I've wanted to test some AI voice models I created with a free AI Text to-speech program Are there any available to test my voice models submitted by /u/mrbeanfan64 [link] [comments]  ( 9 min )

I dont know what it could be the issue, I have tried everything from reinstalling the pip packages, running them on a virtual env, reinstalled CUDA, trying to run on PyCharm instead of VSCode, I got no idea what to do, does anyone know why it could be happening? Maybe something related to environment variables that I maybe messed up after watching like 5 different vids on the topic? What could it be? submitted by /u/someredditguy374632 [link] [comments]  ( 9 min )

Hey guys, so I have googled around and read the documentation but I am still confused between what's the difference between CodeLlama-xb/CodeLlama-xb-Python vs. CodeLlama-xb-instruct? I know the xb model is the base model (for several languages) and the Python model specializes in Python, but what's the instruct model and how is it different from the other 2 models? Would really appreciate your help. Thanks a million! submitted by /u/--leockl-- [link] [comments]  ( 9 min )

I have a blade system with 8 blades. Each blade has 28 cores (e5-2680 v4) and 180gb ram. I would love to run an LLM + Local Files ( kind of like OpenAI does with their api) and run something similar but most “privategpt” LLMs need GPU support and don’t look like they make use of a multi device setup. Anyway to get crunching on CPU on my setup submitted by /u/programmrz [link] [comments]  ( 9 min )

Hello everyone! I'm a truck driver with a passion for creating music. While I'm familiar with basic IT tasks from my personal experiences, I'm new to the tech industry and am eager to break in. I've been exploring AI tools like ChatGPT and have become comfortable navigating GitHub. These experiences make me confident about diving into the tech field. After some research, I'm contemplating three domains: Cloud Technology: It seems stable and beginner-friendly. I'm leaning here primarily because I've heard cybersecurity is saturated. Cybersecurity: Interested but cautious due to market saturation. Generative AI/Data: My true passion lies here, but I've gathered that projects matter more than certifications for entry-level roles in this niche, especially for someone without a degree. Given my background and no technical degree, which field would you suggest I pursue? Should I focus on certifications before taking on projects? I genuinely appreciate any insights! (What do you guys think about this “CloudRoad map” is it good advice?) https://www.madebygps.com/cloudcamp/ [Discussion] submitted by /u/motluv_them [link] [comments]  ( 9 min )

AI Apps are domain-infused, AI/ML-powered applications that continuously learn and adapt with minimal human intervention in helping non-technical users manage data and analytics-intensive operations to deliver well-defined operational outcomes. I originally introduced the idea of a “Data Product Development Canvas” as one of the capstone deliverables (the other being the data science Hypothesis Development Canvas)… Read More »AI apps product development canvas – Part 1 The post AI apps product development canvas – Part 1 appeared first on Data Science Central.  ( 22 min )

I've been dabbling around with pi.ai and I love it and feel like it's only going to get better and better at what it does. I'm curious if there is any interesting new ai bots or discoveries that haven't yet made it mainstream but have tons of use in one way or another for the average consumer.. Things like a language learning ai, or an ai that can read through a textbook or pdf and help you learn it's contents by practically interacting with it to help you comprehend better... there's so many interesting ai things I look forward to seeing submitted by /u/mikel0202 [link] [comments]  ( 9 min )

I am tired of fake ratings and fake reviews in Google maps and I hạve been cheated many times by fake 5 star reviews. I am just looking to find the genuine overạll rating of a place by filtering out 5 star ratings because fake ratings are mostly 5 star and just finding the average of 1-4 ratings so that we can find the overall genuine rating of a Business. Is there any AI tool or any way or any application for that ? Need suggestions on this. submitted by /u/ramesh423 [link] [comments]  ( 9 min )

I'm interested to know if it's possible to mix two faces together, such as Tom Cruise and Brad Pitt, and create a brand new face from those two faces. submitted by /u/Glad-Ad-8953 [link] [comments]  ( 9 min )

Cyberpunk montage I made using Midjourney and RunwayML along with CapCut for the transitions, etc. Here’s some brief tutorial steps. Use Midjourney to make pictures that you like. I would keep it to the default aspect ratio for best results. Use RunwayML to generate 4 second videos of the pics. Once you get some videos you like, save them and upload them to the CapCut app on your phone. submitted by /u/Exitium_Maximus [link] [comments]  ( 9 min )

The UK's AI industry is highly valuable, but its exact worth is difficult to determine. The industry is growing rapidly and has the potential to contribute significantly to the country's economy. However, there are challenges in accurately measuring the value of the AI industry, as it encompasses a wide range of sectors and applications. Some estimates suggest that the UK's AI industry could be worth billions of pounds, with the potential to create thousands of jobs. Investment in research and development, as well as the development of AI talent, are crucial for the growth of the industry. Source : https://www.ft.com/content/eeaa57a3-19ed-45d9-8705-2517c81e60ba submitted by /u/NuseAI [link] [comments]  ( 9 min )

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Hi, I have been accepted onto a PhD at a top UK university and I'm looking for some additional income by working a day a week on other projects. Does anyone have experience of finding something like that to help with costs during a PhD? I have just completed my MSc with really high marks and have published a paper on conversational agents. I have strong general machine learning and data analysis knowledge, strong knowledge of conversational agents and a specialism in reinforcement learning. Before returning to study, I worked for several years in engineering teams so I know how to get stuff done in a professional context too. The only thing is, I have no idea how to actually find something that could be a day a week and pay a worthwhile day rate to help with crazy cost of living. Thank you for any tips! submitted by /u/EDMismyO2 [link] [comments]  ( 9 min )

Is there any insight to be gained about these policy and value loss curves? I hear it’s difficult to judge a good policy by its loss curve, but my agent is hitting a wall in terms of progression over the baseline agents I am evaluating against. For context, my policy is generated by a neural network with the core of it being a dot product of the state embedding against a set of actions embeddings. Any help/understanding would be greatly appreciated. submitted by /u/atomicburn125 [link] [comments]  ( 9 min )

I'm attempting to address an issue related to electricity trading in the day-ahead market. The objective is to submit 24 bids for each hour. Each bid is represented as a vector of length 10, with 5 values indicating the price and the other 5 indicating the quantity of electricity. For instance, given a sample action vector [w, x, y, z, ...], it operates as follows: if the price is greater than w, buy/sell x units of electricity, if the price is greater than y, buy/sell z units of electricity, and so forth. I possess three years' worth of data, including crucial features like weather conditions, coal prices, wind speed, net load, forecasted load, locational marginal price, and more. Initially, I trained a Deep Q-Network (DQN) to tackle this problem, and it's performing quite well. However, the DQN provides a scalar action for each hour, neglecting price points. It acts regardless of the price. I'm aware that I can explore solutions like Proximal Policy Gradient to generate a vector action that includes both electricity unit amounts and prices. I have three questions: Is it possible to solve this problem using Dynamic Programming techniques? While I understand it's not an exceedingly difficult problem, can I expect some results if I attempt to apply DP? How challenging might it be to output a vector instead of a scalar, with the vector being monotonically increasing? What's the recommended approach for a problem of this nature? Is it worthwhile to explore RL and specifically Proximal Policy Optimization? How would you approach such a problem while keeping it simple and avoiding unnecessary complexity? Any guidance or insights would be greatly appreciated. submitted by /u/uonliaquat [link] [comments]  ( 10 min )

I've been watching a tonne of reinforcement learning videos on youtube, and was initially very impressed, but as I watched even more, especially by the same youtubers, I started to notice a distinct issue. Their AI aren't learning to play the game, they're learning to play the level. They'll put in 10s or 100s of hours reinforcing the level. It'll play over and over again, selecting for what works, and dropping what doesn't. And over time, the AI will be amazing at that level. But, if you take that reinforced data, and move to level 2 of that game... it'll be practically useless. When humans play a game, say a brand new human who's never played video games before, we'll use reinforcement learning too. Most everything we do is reinforcement learning. Our brain works on reinforcement. …  ( 11 min )

Adversarial patches and frames are an alternative to the regular $L_p$-constrained adversarial examples. Often, adversarial patches are thought to be more realistic — mirroring graffitis or stickers in the real world. In this article I want to discuss a simple PyTorch implementation and present some results of adversarial patches against adversarial training as well as confidence-calibrated adversarial training. The post Adversarial Patches and Frames in PyTorch appeared first on David Stutz.  ( 7 min )

Sharifa Alghowinem, a research scientist at the Media Lab, explores personal robot technology that explains emotions in English and Arabic.  ( 8 min )

Earlier I wrote a post showing what happens when you start with an equilateral triangle, then repeatedly subdivide it into smaller and smaller triangles by drawing lines from the centroid (barycenter) to each of the vertices. I mentioned in that post that I moved the code for finding the center to its own function because […] How you define center matters a lot first appeared on John D. Cook.  ( 5 min )

Hey everyone, I'm Raynel, working on a CRM platform and actively considering the future integration of AI. One idea I'm particularly excited about is anticipatory customer support. The goal is to proactively address customer needs, perhaps even before they realize them, using ML-driven insights. Has anyone delved into this concept or tried implementing it? I'd love to hear thoughts, potential pitfalls, or even success stories. Thanks in advance for your insights! submitted by /u/bess_point [link] [comments]  ( 9 min )

I used to build on top of conda, or make docker containers with conda package manager, now that is so broken it is impossible to install old pytorch on a fresh environment. Any way to replace the existing requirements with something better? submitted by /u/AardvarkNo6658 [link] [comments]  ( 9 min )

I thought it would be interesting to open this discussion, I would like to hear what you guys have to say about that. I know that ML folks use statistics all the time, but I am often under the impression that it's mostly the basics. I understand that inference and prediction are inherently different, but I would assume the common knowledge will be much more vast. What is your perspective on that? Am I just missing the point? Is there room for improvement in the future? Do you think statistics literacy (advanced, not ANOVA or so) is common among ML practitioners? submitted by /u/pyepyepie [link] [comments]  ( 9 min )

I wish to generate visuals that are synced with beats etc of the music to offer the "sensory synchronization" effect where visuals closely sync with the music. I have found Lucid sonic dreams, but it appears to be quite buggy and likely no longer supported. any recommendations for tools I can leverage for a hobby->serious project of generating visuals synced with music. edit: I looked into simple approaches using fft like described here. But I was hoping there are newer generative ai techniques we could leverage. submitted by /u/bluzkluz [link] [comments]  ( 9 min )

I'm starting a research lab at a Top 25 research university next year and my research agenda is focused on AI/DL for a scientific domain, I have to come up with a plan and budget for my software/hardware needs. My Context: I have experience setting up linux systems, building computers by myself and training DL models (CNNs/GNNs/LLMs) in a corporate setting. I am venturing to the academic world where resources are more constrained and so I am wondering if there are any guides, tips on setting up a research computer lab that does AI. I do not expect to train from scratch an LLM but maybe finetuning an small LLM. I might also need to do a lot parallelizable IO work to preprocess data. I will talk with the IT department of the university but would like to have some ideas before that conversation. I would appreciate any tips or thoughts, particularly on: * How many GPUs/CPUs to buy? Balancing cost/compute. * Recommended software for managing resources. * Running 100s-1ks of CPU jobs in parallel. * Local compute cluster vs buying compute online. * Hyperparameter optimization and logging of metrics. * Anything else you can think of? submitted by /u/prof_is_training [link] [comments]  ( 9 min )

Location: https://huggingface.co/datasets/euirim/goodwiki Hi everyone, just wanted to share a dataset I've been working on for use in a personal project! GoodWiki is a 179 million token dataset of English Wikipedia articles collected on September 4, 2023, that have been marked as Good or Featured by Wikipedia editors. The dataset provides these articles in GitHub-flavored Markdown format, preserving layout features like lists, code blocks, math, and block quotes, unlike many other public Wikipedia datasets. Articles are accompanied by a short description of the page as well as any associated categories. Thanks to a careful conversion process from wikicode, the markup language used by Wikipedia, articles in GoodWiki are generally faithful reproductions of the corresponding original Wikipedia pages, minus references, files, infoboxes, and tables. Curated template transclusion and HTML tag handling have minimized instances where entire words and phrases are missing mid-sentence like in other public Wikipedia datasets. GoodWiki is more than 1.5 times larger (when compared using the same tokenizer) than the widely used WikiText-103 dataset by Merity et al., even after excluding article descriptions. Also limited to articles marked as Good or Featured, WikiText inspired GoodWiki. submitted by /u/euirim [link] [comments]  ( 9 min )

I wanna learn ML and i love this field there is people told me that the opportunities in this field is few (i live in egypt btw)and in USA also they suffer from a lack of opportunities in this field so is that true and should i choose another field or not because i really wanna learn ML . submitted by /u/Opening-Being-7692 [link] [comments]  ( 9 min )

Im currently working on deploying an ML model that predicts NFL MVPs (two possible outputs: 0 for not MVP and 1 for MVP). That being said, during deployment it is currently predicting 0 for all inputs regardless of how varied the inputs may be. However, during the testing phase my model had a varied accuracy rate of 75%-85%. I'm beginning to think that it is very likely that during the testing phase the model was predicting 0 for everything and just getting these higher accuracy rates because of the dominance of non-MVPs within my dataset. This all being said, I'm a noob to ML and decided it'd be best to come on here for help. Is that the likely reason and if so how do I go about fixing it? Furthermore, what other issues could cause this and how would I go about fixing it? submitted by /u/saggyboobsarecooltoo [link] [comments]  ( 9 min )

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I recently discovered a python library called 'Petals' that should run most LLM models (LLaMA2, Stable Beluga) on some kind of "torrent" for machine learning on your device: https://github.com/bigscience-workshop/petals/, https://www.youtube.com/watch?v=8jEGVaRKmFc Furthermore, if you participate as a "server" in this "torrent" you can be rewarded (according to YouTube) Sounds too good to be true, so I didn't try it yet. Does anyone have experience with it? Can I get a virus from there? Is it not a scam? submitted by /u/Degenerat666 [link] [comments]  ( 9 min )

PostgresML takes Retrieval Augmented Generation (RAG) a step further, by running the models and vector (or btree) indexes in the same process space that also caches the data, so the retrieval step doesn't require any networking or data (de)serialization. This makes it significantly faster and more reliable than other architectures, which is important for online or interactive applications. For lack of a better term, I'm referring to this as Unified Retrieval Augmented Generation (URAG). The closest I can find to this in the literature is TABR which utilizes recall to improve the performance of LLMs relative to tree based models, although this particular application of Retrieval is extremely computationally expensive for a slight performance improvement, unlike RAG for LLMs that seems to provide significant new capabilities to the model. Is there more research going on for what I'd refer to as URAG systems? We're building open source Python & JS SDKs to make the underlying SQL API more accessible, but I'm wondering if this community, or the academic community has already done more work in this area that we should be aware of. submitted by /u/something_cleverer [link] [comments]  ( 9 min )

With all the recent advancements in LLMs, how does chatbot development look like in practise? Suppose I want a chatbot to help with customer service. Can I then just collect some frequent / common questions about items the company might offer for sale and use a pretrained LLM to answer these questions correctly via transfer learning? Is there some effort that goes into explicit knowledge storage? submitted by /u/Blutorangensaft [link] [comments]  ( 9 min )

Hey everyone! I recently started a project where I make AI-powered characters that create content autonomously on social media. The first character that I launched yesterday is your typical valley girl - Alix - who hangs around tech and crypto people. She browses Twitter all day and writes tweets with her opinion of the stuff she comes by. With a unique mood generated for her every day, it's always a gamble to see who lands on her good or bad side. I would really appreciate it if you check her out and let me know what you think! I'm interested in further developing the project and making these synthetic entities equivalent to real people in entertaiment and companionship. Her twitter - https://twitter.com/alix_H2O submitted by /u/GuaranteeAny2894 [link] [comments]  ( 9 min )

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Problem description For ongoing research I'm defining a decision problem in the form of an MDP. In a simple form, this MDP can be solved via Dynamic Programming. Of course I would like to scale up my MDP by including a sensor simulation, which renders the MDP to such a complexity that my next step is to use a DQN. My challenge is that I'm not sure how to represent my more complex MDP in a fitting DQN. Quite frankly, the more I look into DQN's, the more I wonder if my MDP is correctly defined. It would be great to hear your views and suggestions if you like to share. Definition of the MDP The MDP aims at detecting and chasing away a mosquito. For each distance step (discrete distances although it should be continuous), for instance Si, we cycle through an episode: we detect the mosquito…  ( 10 min )

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Is it normal to achieve a validation accuracy so much higher than the training accuracy? I am using transfer learning to train a convNeXtBaseV1 model on my dataset. I got a training accuracy of 82.9% and a much higher validation accuracy of 97.14%. My dataset is around 9600 medical images and it is balanced between 3 classes. I splitted it into 80% training 10% testing and 10% validation submitted by /u/Different_Hat5643 [link] [comments]  ( 9 min )

Hi. I tried tortoise tts. It is good for cloning how the voice sounds but not the original accent. Are there any open source technologies that can also copy the accent correctly? Please do suggest. Thank you! submitted by /u/salehxoxo [link] [comments]  ( 9 min )

Article | Notebook | GitHub There are a rapidly growing number of options in the vector database space. One of the more recent developments is the creation of new vector index plugins for traditional database systems such as pgvector. This is reminiscent of the discussion back in the mid 2010s on whether one should use full text search in the database or sync with an external system such as Elasticsearch. Just as with full text search, it will be tough for vector indexing in the database to compete with the more dedicated solutions past the simple use cases. For example, according the ANN-Benchmarks, the dedicated vector solutions score much higher. The desire to reduce stack complexity and the maturity of systems like Postgres make strong arguments to try to find a way to do it all in the database. The referenced article above proposes a way to integrate existing databases like PostgreSQL with vector indexes such as Faiss, Hnswlib, external vector databases and even keyword indexes like Elasticsearch. This opens up the possibility of combining Postgres features such as fine-grained access control with the performance of a dedicated vector index. ​ submitted by /u/davidmezzetti [link] [comments]  ( 9 min )

I am working on clustering groups of almost identical (but time shifted) signals together. These clusters will have varying sizes, and I don´t know what the final number of clusters will be. The database consists of up to 100 thousand signals (represented by 4000 long vectors as read from individual text files) which are already quite similar to each other. Ideally I would just make a huge cross-correlation matrix but that is too computationally expensive. So is DTW. What I have done is to apply the fast fourier transform to get it into the frequency domain and therefore get rid of that time shift. Then I would apply a clustering algorithm. I have tried DBSCAN and hierarchical agglomerative clustering which work relatively well but don’t scale well to datasets of this size. Affinity propagation is quick and works okish but I don’t know how to optimise it. Does anyone have any recommendation on which algorithm to use and how to optimise it? Was my idea to apply the fft good? I am not a computer scientist so I am really out of my element. submitted by /u/Bertz-2- [link] [comments]  ( 9 min )

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I was given following problem in one of the machine learning interviews. I think I messed up there. Need your approach in answering this question. Problem Description: Imagine you are working for a manufacturing company that operates a large fleet of industrial machines. These machines are critical to production, and unexpected breakdowns can result in significant downtime and financial losses. Your task is to develop a predictive maintenance model using machine learning to predict when a machine is likely to fail so that maintenance can be performed just in time to prevent a breakdown. Data: You are provided with historical data for each machine, including sensor readings, maintenance logs, and failure records. The dataset is extensive, containing millions of data points over several ye…  ( 10 min )

Does anyone know what this means if when you import your audio clip and the model you want to use it gets stuck in the queue for over 2 and a half hours? I know that can't be right but I seemed to have followed all the guides correctly not sure what it could be :'( help greatly appreciated submitted by /u/StuntGuy [link] [comments]  ( 9 min )

I just saw a tweet from Dave Richeson saying I remember as a kid calculating the size difference (diameter) of a belt between each hole. Now I think about it every time I wear a belt. Holes 1 inch apart change the diameter by about one-third of an inch (1/π). [Assuming people have a circular […] Belt around an elliptical waist first appeared on John D. Cook.  ( 6 min )

The other day I saw where Cliff Pickover tweeted some images of triangles recursively subdivided by adding a point to the barycenter of each triangle. The images were not what I expected, so I wanted to reproduce the images to look into this further. Here are the first three steps: I set the alpha value […] Recursive triangle subdivision first appeared on John D. Cook.  ( 5 min )

"AI Unleashed: This Week's Top 15 News and Breakthroughs in Artificial Intelligence" 1"X's Data Revolution: Your Biometrics and Career Fueling the AI of Tomorrow!" In a recent privacy policy shake-up, X has just dropped a bombshell. They're not just interested in your regular data anymore; they're diving deep into the world of biometrics, job histories, and education backgrounds. And that's not all! Another corner of their revamped policy hints at a grand plan: they want to fuse this treasure trove of data with publicly available info to supercharge their machine learning and AI models. This isn't your run-of-the-mill update; it's a quantum leap for X in their quest to build the ultimate AI system. They're not just pushing boundaries; they're smashing through them. By expanding their da…  ( 16 min )

The artificial intelligence technology behind ChatGPT was built in Iowa, specifically in the watershed of the Raccoon and Des Moines rivers. Microsoft-backed OpenAI needed a lot of water to cool its powerful supercomputer as it taught its AI systems how to mimic human writing. Leading tech developers like Microsoft, OpenAI, and Google have acknowledged the high costs associated with the growing demand for AI tools, including expensive semiconductors and increased water consumption. Microsoft disclosed a 34% spike in global water consumption, largely attributed to its AI research. A researcher estimates that ChatGPT uses 500 milliliters of water for every series of prompts or questions. Source : https://news.yahoo.com/artificial-intelligence-technology-behind-chatgpt-131421382.html Summarized by Nuse AI submitted by /u/NuseAI [link] [comments]  ( 9 min )

Based on a bit of research and a lot of gut feeling, I offer the following speculation: if you self-trained an LLM with a Python interpreter or Java compiler in a feedback loop where it learned from its own mistakes then it could become dramatically better at coding. It's actually a miracle that they are "decent" at coding despite getting virtually no feedback from an interpreter or compiler. one could train not merely on input and output, but also on an execution trace so the LLM learned HOW the interpreter got the result one could also train the model on how to install and invoke open source software and thus it would learn about a variety of languages, versions and runtimes this might also improve its logical reasoning skills in general Admittedly, running programs is a lot more expensive than doing simple next-word prediction on pre-existing texts. But on the other hand, a corpus of a million program executions can also be used to train future LLMs. You can keep the execution information forever and re-use it as traditional next-token prediction input. submitted by /u/Smallpaul [link] [comments]  ( 9 min )

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So LLM's like ChatGPT have been around for a while now, and have good APIs, and also voice to text dictation and text to voice generation are close to perfect these days... are there any services that join them all together? I'd like to chat with ChatGPT or similar by talking to my smart speaker a la Google Home, and have it respond by speaking, and to be able to reply and continue the conversation. Does anyone know if this exists yet? submitted by /u/singeblanc [link] [comments]  ( 9 min )

hey guys, im trying to subtitle a spanish video to have english subtitles, does anyone know a good way to do it for free? submitted by /u/deletemkw [link] [comments]  ( 9 min )

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The media often portrays lawmakers as unable to regulate artificial intelligence (AI) due to its complexity and evolving nature. This narrative overlooks the responsibility of lawmakers and their regulatory inertia. The media frames AI regulation as a matter of technical knowledge rather than moral consideration. The New York Times highlights the slow congressional response to new technologies and the potential influence of lawmakers' financial interests in AI companies. The media fails to question why lawmakers, who have profited from AI, cannot apply their knowledge to regulate it. This lack of critical information in news sources defends the inaction of lawmakers. Source : https://fair.org/home/in-ai-regulation-coverage-media-let-lawmakers-off-the-hook/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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TIME just picked a list of 100 Most Influential People in AI.[1] AI Startup Imbue Tops $1 Billion Valuation After Funding from Nvidia.[2] Microsoft offers legal protection for AI copyright infringement challenges.[3] US chipmaker Nvidia Corp. on Friday announced separate partnerships with Reliance and Tata group companies to help them develop AI-powered supercomputers, AI clouds and generative AI applications.[4] Sources: [1] https://time.com/6311323/how-we-chose-time100-ai/ [2] https://www.bloomberg.com/news/articles/2023-09-07/ai-startup-imbue-tops-1-billion-valuation-after-funding-from-nvidia?embedded-checkout=true [3] https://arstechnica.com/information-technology/2023/09/microsoft-offers-legal-protection-for-ai-copyright-infringement-challenges/ [4] https://www.livemint.com/companies/news/reliance-tata-cos-sign-ai-partnerships-with-nvidia-11694198851600.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Does anyone know what this means if when you import your audio clip and the model you want to use it gets stuck in the queue for over 2 and a half hours? I know that can't be right but I seemed to have followed all the guides correctly not sure what it could be :'( help greatly appreciated submitted by /u/StuntGuy [link] [comments]  ( 9 min )

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Healthcare technology disruptor Paige is teaming up with Microsoft in the race against cancer. The collaboration aims to revolutionize cancer diagnosis and patient care by building the largest image-based artificial intelligence model for digital pathology and oncology. To stay one step ahead in AI transformations, subscribe here. Transforming cancer imaging Using Microsoft’s advanced supercomputing infrastructure, Paige aims to take cancer imaging to the next level. By combining its deep AI expertise with Microsoft’s enormous computing power, this model augments accuracy and brings in novel capabilities in cancer diagnostics. In the next phase, Paige will incorporate up to four million digitized microscopy slides from its petabyte-scale clinical data archive. A milestone in oncology The Paige and Microsoft partnership is a game changer in advancing healthcare, equipping physicians with unprecedented insights into cancer pathology. Paige is the first company to receive FDA approval for a clinical AI application in digital pathology. The technology is set to increase diagnostic confidence, productivity, and expand treatment options for patients worldwide. (source) P.S. If you love these analyses, I write a free newsletter to track the most significant news and research in AI and tech. Experts from Google, Meta, OpenAI, and more read it daily. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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I'm looking for some ideas on Multi Agent RL that preferably involve Robotics. I've came up with two ideas based on essentially similar themes: 1) Multiple robots tasked with cleaning a large room (with obstacles) 2) Multiple robots tasked with a search and rescue like mission in a particular area. Both are basically applications of n agents trying to collectively cover a region. Can someone recommend some frameworks and libraries that can allow me to simulate these ideas? Also, I'd love to hear some other ideas as well which use multi-agent RL for robotic applications. For now I'm only targeting a simulation based project. If I get time later I'd love to implement them on hardware as well. Thanks in advance! submitted by /u/esem29 [link] [comments]  ( 9 min )

New introduction textbook titled "Multi-Agent Reinforcement Learning: Foundations and Modern Approaches" by Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer, to be published by MIT Press. The book draft can be downloaded here: https://www.marl-book.com/ ​ submitted by /u/vuttigiquoje-4292 [link] [comments]  ( 9 min )

To review IPPO vs. MAPPO, first, let's revisit the paper "Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?". ( https://arxiv.org/abs/2011.09533 ) The paper shows that simply applying PPO to the SMAC task to construct an IPPO (independent PPO) algorithm like IQL can surpass QMIX on SMAC. IPPO demonstrates the effectiveness of applying PPO to multi-agent systems. The paper further extends IPPO to MAPPO. The difference is that the critic of PPO uses the global state instead of the observation as input. Surprisingly, the global information does not enhance the actual performance of IPPO. ​ https://preview.redd.it/1wqqlj9z05nb1.png?width=1440&format=png&auto=webp&s=0d6ca1faa0e872151abdb6bb1e48884c6b51e71a ​ https://preview.redd.it/ei9uacl015nb1.png?width=1440&fo…  ( 10 min )

Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scaling up detection-oriented data often require manual collection or generative models to obtain target images, followed by data augmentation and labeling to produce training pairs, which are costly, complex, or lacking diversity. To address these issues, we presentDiffusionEngine (DE), a data scaling-up engine that provides high-quality detection-oriented training pairs in a single stage. DE consists of a pre-trained diffusion model and an effective Detection-Adapter, contributing to generating scalable, diverse and generalizable detection data in a plug-and-play manner. Detection-Adapter is learned to align the implicit semantic and location knowledge in off-the-shelf diffusion models with detection-aware signals to make better bounding-box predictions. Additionally, we contribute two datasets, i.e., COCO-DE and VOC-DE, to scale up existing detection benchmarks for facilitating follow-up research. Extensive experiments demonstrate that data scaling-up via DE can achieve significant improvements in diverse scenarios, such as various detection algorithms, self-supervised pre-training, data-sparse, label-scarce, cross-domain, and semi-supervised learning. For example, when using DE with a DINO-based adapter to scale up data, mAP is improved by 3.1% on COCO, 7.6% on VOC, and 11.5% on Clipart.  ( 2 min )

Researchers are solving the challenges of spatial-temporal prediction by combining Federated Learning (FL) and graph models with respect to the constrain of privacy and security. In order to make better use of the power of graph model, some researchs also combine split learning(SL). However, there are still several issues left unattended: 1) Clients might not be able to access the server during inference phase; 2) The graph of clients designed manually in the server model may not reveal the proper relationship between clients. This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues. For the first issue, the server model of M$^3$FGM employs a MaskNode layer to simulate the case of clients being offline. We also redesign the decoder of the client model using a dual-sub-decoders structure so that each client model can use its local data to predict independently when offline. As for the second issue, a new GNN layer named Multi-Granularity Message Passing (MGMP) layer enables each client node to perceive global and local information. We conducted extensive experiments in two different scenarios on two real traffic datasets. Results show that M$^3$FGM outperforms the baselines and variant models, achieves the best results in both datasets and scenarios.  ( 3 min )

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples.  ( 2 min )

There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS. We present an approach to proper learning of LDS, which in spite of the non-convexity of the problem, guarantees global convergence of numerical solutions to a least-squares estimator. We present promising computational results.  ( 2 min )

Graph neural networks (GNNs) have gained an increasing amount of popularity due to their superior capability in learning node embeddings for various graph inference tasks, but training them can raise privacy concerns. To address this, we propose using link local differential privacy over decentralized nodes, enabling collaboration with an untrusted server to train GNNs without revealing the existence of any link. Our approach spends the privacy budget separately on links and degrees of the graph for the server to better denoise the graph topology using Bayesian estimation, alleviating the negative impact of LDP on the accuracy of the trained GNNs. We bound the mean absolute error of the inferred link probabilities against the ground truth graph topology. We then propose two variants of our LDP mechanism complementing each other in different privacy settings, one of which estimates fewer links under lower privacy budgets to avoid false positive link estimates when the uncertainty is high, while the other utilizes more information and performs better given relatively higher privacy budgets. Furthermore, we propose a hybrid variant that combines both strategies and is able to perform better across different privacy budgets. Extensive experiments show that our approach outperforms existing methods in terms of accuracy under varying privacy budgets.  ( 3 min )

The atmosphere affects humans in a multitude of ways, from loss of life due to adverse weather effects to long-term social and economic impacts on societies. Computer simulations of atmospheric dynamics are, therefore, of great importance for the well-being of our and future generations. Here, we propose AtmoRep, a novel, task-independent stochastic computer model of atmospheric dynamics that can provide skillful results for a wide range of applications. AtmoRep uses large-scale representation learning from artificial intelligence to determine a general description of the highly complex, stochastic dynamics of the atmosphere from the best available estimate of the system's historical trajectory as constrained by observations. This is enabled by a novel self-supervised learning objective and a unique ensemble that samples from the stochastic model with a variability informed by the one in the historical record. The task-independent nature of AtmoRep enables skillful results for a diverse set of applications without specifically training for them and we demonstrate this for nowcasting, temporal interpolation, model correction, and counterfactuals. We also show that AtmoRep can be improved with additional data, for example radar observations, and that it can be extended to tasks such as downscaling. Our work establishes that large-scale neural networks can provide skillful, task-independent models of atmospheric dynamics. With this, they provide a novel means to make the large record of atmospheric observations accessible for applications and for scientific inquiry, complementing existing simulations based on first principles.  ( 3 min )

With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at \url{https://github.com/Masuyama-lab/FCAC}.  ( 2 min )

As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions. We suggest a novel approach that models how explanation characteristics shift when affected by distribution shifts. We find that the modeling of explanation shifts can be a better indicator for detecting out-of-distribution model behaviour than state-of-the-art techniques. We analyze different types of distribution shifts using synthetic examples and real-world data sets. We provide an algorithmic method that allows us to inspect the interaction between data set features and learned models and compare them to the state-of-the-art. We release our methods in an open-source Python package, as well as the code used to reproduce our experiments.  ( 2 min )

While a key component to the success of deep learning is the availability of massive amounts of training data, medical image datasets are often limited in diversity and size. Transfer learning has the potential to bridge the gap between related yet different domains. For medical applications, however, it remains unclear whether it is more beneficial to pre-train on natural or medical images. We aim to shed light on this problem by comparing initialization on ImageNet and RadImageNet on seven medical classification tasks. Our work includes a replication study, which yields results contrary to previously published findings. In our experiments, ResNet50 models pre-trained on ImageNet tend to outperform those trained on RadImageNet. To gain further insights, we investigate the learned representations using Canonical Correlation Analysis (CCA) and compare the predictions of the different models. Our results indicate that, contrary to intuition, ImageNet and RadImageNet may converge to distinct intermediate representations, which appear to diverge further during fine-tuning. Despite these distinct representations, the predictions of the models remain similar. Our findings show that the similarity between networks before and after fine-tuning does not correlate with performance gains, suggesting that the advantages of transfer learning might not solely originate from the reuse of features in the early layers of a convolutional neural network.

This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particularly useful for a range of problems in this domain, such as the covering technique, the Hanson-Wright Inequality and the method of self-normalized martingales. We then employ these tools to give streamlined proofs of the performance of various least-squares based estimators for identifying the parameters in autoregressive models. We conclude by sketching out how the ideas presented herein can be extended to certain nonlinear identification problems.

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of $O(k^2)$. In experiments on real data with 30000 samples and $k$=100, we observed a 10464$\times$ speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.

Typical technique in knowledge distillation (KD) is regularizing the learning of a limited capacity model (student) by pushing its responses to match a powerful model's (teacher). Albeit useful especially in the penultimate layer and beyond, its action on student's feature transform is rather implicit, limiting its practice in the intermediate layers. To explicitly embed the teacher's knowledge in feature transform, we propose a learnable KD layer for the student which improves KD with two distinct abilities: i) learning how to leverage the teacher's knowledge, enabling to discard nuisance information, and ii) feeding forward the transferred knowledge deeper. Thus, the student enjoys the teacher's knowledge during the inference besides training. Formally, we repurpose 1x1-BN-ReLU-1x1 convolution block to assign a semantic vector to each local region according to the template (supervised by the teacher) that the corresponding region of the student matches. To facilitate template learning in the intermediate layers, we propose a novel form of supervision based on the teacher's decisions. Through rigorous experimentation, we demonstrate the effectiveness of our approach on 3 popular classification benchmarks. Code is available at: https://github.com/adagorgun/letKD-framework

Multiagent systems aim to accomplish highly complex learning tasks through decentralised consensus seeking dynamics and their use has garnered a great deal of attention in the signal processing and computational intelligence societies. This article examines the behaviour of multiagent networked systems with nonlinear filtering/learning dynamics. To this end, a general formulation for the actions of an agent in multiagent networked systems is presented and conditions for achieving a cohesive learning behaviour is given. Importantly, application of the so derived framework in distributed and federated learning scenarios are presented.

The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations. In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design. In 2) LLMs can assist in contextualization, intent classification to prevent conversational breakdown and handle out-of-scope questions, auto-correcting utterances, rephrasing responses, formulating disambiguation questions, summarization, and enabling closed question-answering capabilities. We conducted informal experiments with GPT-4 in the private banking domain to demonstrate the scenarios above with a practical example. Companies may be hesitant to replace their pipeline-based agents with LLMs entirely due to privacy concerns and the need for deep integration within their existing ecosystems. A hybrid approach in which LLMs' are integrated into the pipeline-based agents allows them to save time and costs of building and running agents by capitalizing on the capabilities of LLMs while retaining the integration and privacy safeguards of their existing systems.

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? Information theory gives one answer: To maximize the information in the model, select the most likely objective function or whichever represents the error in the fewest bits. To evaluate different objectives, transform them into likelihood functions. As likelihoods, their relative magnitudes represent how much we should prefer one objective versus another, and the log of their magnitude represents the expected uncertainty of the model.

Decisions made by machine learning models may have lasting impacts over time, making long-term fairness a crucial consideration. It has been shown that when ignoring the long-term effect, naively imposing fairness criterion in static settings can actually exacerbate bias over time. To explicitly address biases in sequential decision-making, recent works formulate long-term fairness notions in Markov Decision Process (MDP) framework. They define the long-term bias to be the sum of static bias over each time step. However, we demonstrate that naively summing up the step-wise bias can cause a false sense of fairness since it fails to consider the importance difference of different time steps during transition. In this work, we introduce a long-term fairness notion called Equal Long-term Benefit Rate (ELBERT), which explicitly considers varying temporal importance and adapts static fairness principles to the sequential setting. Moreover, we show that the policy gradient of Long-term Benefit Rate can be analytically reduced to standard policy gradient. This makes standard policy optimization methods applicable for reducing the bias, leading to our proposed bias mitigation method ELBERT-PO. Experiments on three sequential decision making environments show that ELBERT-PO significantly reduces bias and maintains high utility. Code is available at https://github.com/Yuancheng-Xu/ELBERT.

Low Rank Decomposition (LRD) is a model compression technique applied to the weight tensors of deep learning models in order to reduce the number of trainable parameters and computational complexity. However, due to high number of new layers added to the architecture after applying LRD, it may not lead to a high training/inference acceleration if the decomposition ranks are not small enough. The issue is that using small ranks increases the risk of significant accuracy drop after decomposition. In this paper, we propose two techniques for accelerating low rank decomposed models without requiring to use small ranks for decomposition. These methods include rank optimization and sequential freezing of decomposed layers. We perform experiments on both convolutional and transformer-based models. Experiments show that these techniques can improve the model throughput up to 60% during training and 37% during inference when combined together while preserving the accuracy close to that of the original models

All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to address Occam's razor. FixFit, our new method, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model's degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In two use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.

Recent advances in the field of deep learning and impressive performance of deep neural networks (DNNs) for perception have resulted in an increased demand for their use in automated driving (AD) systems. The safety of such systems is of utmost importance and thus requires to consider the unique properties of DNNs. In order to achieve safety of AD systems with DNN-based perception components in a systematic and comprehensive approach, so-called safety concerns have been introduced as a suitable structuring element. On the one hand, the concept of safety concerns is -- by design -- well aligned to existing standards relevant for safety of AD systems such as ISO 21448 (SOTIF). On the other hand, it has already inspired several academic publications and upcoming standards on AI safety such as ISO PAS 8800. While the concept of safety concerns has been previously introduced, this paper extends and refines it, leveraging feedback from various domain and safety experts in the field. In particular, this paper introduces an additional categorization for a better understanding as well as enabling cross-functional teams to jointly address the concerns.

Large language models, particularly those akin to the rapidly progressing GPT series, are gaining traction for their expansive influence. While there is keen interest in their applicability within medical domains such as psychology, tangible explorations on real-world data remain scant. Concurrently, users on social media platforms are increasingly vocalizing personal sentiments; under specific thematic umbrellas, these sentiments often manifest as negative emotions, sometimes escalating to suicidal inclinations. Timely discernment of such cognitive distortions and suicidal risks is crucial to effectively intervene and potentially avert dire circumstances. Our study ventured into this realm by experimenting on two pivotal tasks: suicidal risk and cognitive distortion identification on Chinese social media platforms. Using supervised learning as a baseline, we examined and contrasted the efficacy of large language models via three distinct strategies: zero-shot, few-shot, and fine-tuning. Our findings revealed a discernible performance gap between the large language models and traditional supervised learning approaches, primarily attributed to the models' inability to fully grasp subtle categories. Notably, while GPT-4 outperforms its counterparts in multiple scenarios, GPT-3.5 shows significant enhancement in suicide risk classification after fine-tuning. To our knowledge, this investigation stands as the maiden attempt at gauging large language models on Chinese social media tasks. This study underscores the forward-looking and transformative implications of using large language models in the field of psychology. It lays the groundwork for future applications in psychological research and practice.

Accurate segmentation of punctate white matter lesions (PWMLs) are fundamental for the timely diagnosis and treatment of related developmental disorders. Automated PWMLs segmentation from infant brain MR images is challenging, considering that the lesions are typically small and low-contrast, and the number of lesions may dramatically change across subjects. Existing learning-based methods directly apply general network architectures to this challenging task, which may fail to capture detailed positional information of PWMLs, potentially leading to severe under-segmentations. In this paper, we propose to leverage the idea of counterfactual reasoning coupled with the auxiliary task of brain tissue segmentation to learn fine-grained positional and morphological representations of PWMLs for accurate localization and segmentation. A simple and easy-to-implement deep-learning framework (i.e., DeepPWML) is accordingly designed. It combines the lesion counterfactual map with the tissue probability map to train a lightweight PWML segmentation network, demonstrating state-of-the-art performance on a real-clinical dataset of infant T1w MR images. The code is available at \href{https://github.com/ladderlab-xjtu/DeepPWML}{https://github.com/ladderlab-xjtu/DeepPWML}.

Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ejection fraction (EF), where lower EF is associated with cardiomyopathy. Echocardiography is a popular diagnostic tool in cardiology, with ultrasound being a low-cost, real-time, and non-ionizing technology. However, human assessment of echocardiograms for calculating EF is time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we propose using the M(otion)-mode of echocardiograms for estimating the EF and classifying cardiomyopathy. We generate multiple artificial M-mode images from a single echocardiogram and combine them using off-the-shelf model architectures. Additionally, we extend contrastive learning (CL) to cardiac imaging to learn meaningful representations from exploiting structures in unlabeled data allowing the model to achieve high accuracy, even with limited annotations. Our experiments show that the supervised setting converges with only ten modes and is comparable to the baseline method while bypassing its cumbersome training process and being computationally much more efficient. Furthermore, CL using M-mode images is helpful for limited data scenarios, such as having labels for only 200 patients, which is common in medical applications.

We introduce two new extensions to the beam search algorithm based on conformal predictions (CP) to produce sets of sequences with theoretical coverage guarantees. The first method is very simple and proposes dynamically-sized subsets of beam search results but, unlike typical CP procedures, has an upper bound on the achievable guarantee depending on a post-hoc calibration measure. Our second algorithm introduces the conformal set prediction procedure as part of the decoding process, producing a variable beam width which adapts to the current uncertainty. While more complex, this procedure can achieve coverage guarantees selected a priori. We provide marginal coverage bounds for each method, and evaluate them empirically on a selection of tasks drawing from natural language processing and chemistry.

Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity arises from the need for multi-fingered robotic hands to dynamically establish and break contacts, balance non-prehensile forces, and control large degrees of freedom. Reinforcement learning (RL) offers a promising approach due to its general applicability and capacity to autonomously acquire optimal manipulation strategies. However, its real-world application is often hindered by the necessity to generate a large number of samples, reset the environment, and obtain reward signals. In this work, we introduce an efficient system for learning dexterous manipulation skills with RL to alleviate these challenges. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. This combination allows us to utilize data from different tasks or objects as a starting point for training new tasks, significantly improving learning efficiency. Additionally, our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy as well as learned reward functions, eliminating the need for manual resets and reward engineering. We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand. (Videos: https://sites.google.com/view/reboot-dexterous)

Explainable Artificial Intelligence (XAI) has become increasingly significant for improving the interpretability and trustworthiness of machine learning models. While saliency maps have stolen the show for the last few years in the XAI field, their ability to reflect models' internal processes has been questioned. Although less in the spotlight, example-based XAI methods have continued to improve. It encompasses methods that use examples as explanations for a machine learning model's predictions. This aligns with the psychological mechanisms of human reasoning and makes example-based explanations natural and intuitive for users to understand. Indeed, humans learn and reason by forming mental representations of concepts based on examples. This paper provides an overview of the state-of-the-art in natural example-based XAI, describing the pros and cons of each approach. A "natural" example simply means that it is directly drawn from the training data without involving any generative process. The exclusion of methods that require generating examples is justified by the need for plausibility which is in some regards required to gain a user's trust. Consequently, this paper will explore the following family of methods: similar examples, counterfactual and semi-factual, influential instances, prototypes, and concepts. In particular, it will compare their semantic definition, their cognitive impact, and added values. We hope it will encourage and facilitate future work on natural example-based XAI.

Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. Yet, our understanding of this phenomenon stems from a rather fragmented pool of knowledge; at present, there are a handful of attacks, each with disparate assumptions in threat models and incomparable definitions of optimality. In this paper, we propose a systematic approach to characterize worst-case (i.e., optimal) adversaries. We first introduce an extensible decomposition of attacks in adversarial machine learning by atomizing attack components into surfaces and travelers. With our decomposition, we enumerate over components to create 576 attacks (568 of which were previously unexplored). Next, we propose the Pareto Ensemble Attack (PEA): a theoretical attack that upper-bounds attack performance. With our new attacks, we measure performance relative to the PEA on: both robust and non-robust models, seven datasets, and three extended lp-based threat models incorporating compute costs, formalizing the Space of Adversarial Strategies. From our evaluation we find that attack performance to be highly contextual: the domain, model robustness, and threat model can have a profound influence on attack efficacy. Our investigation suggests that future studies measuring the security of machine learning should: (1) be contextualized to the domain & threat models, and (2) go beyond the handful of known attacks used today.

Accurate acquisition of crowd flow at Points of Interest (POIs) is pivotal for effective traffic management, public service, and urban planning. Despite this importance, due to the limitations of urban sensing techniques, the data quality from most sources is inadequate for monitoring crowd flow at each POI. This renders the inference of accurate crowd flow from low-quality data a critical and challenging task. The complexity is heightened by three key factors: 1) \emph{The scarcity and rarity of labeled data}, 2) \emph{The intricate spatio-temporal dependencies among POIs}, and 3) \emph{The myriad correlations between precise crowd flow and GPS reports}. To address these challenges, we recast the crowd flow inference problem as a self-supervised attributed graph representation learning task and introduce a novel \underline{C}ontrastive \underline{S}elf-learning framework for \underline{S}patio-\underline{T}emporal data (\model). Our approach initiates with the construction of a spatial adjacency graph founded on the POIs and their respective distances. We then employ a contrastive learning technique to exploit large volumes of unlabeled spatio-temporal data. We adopt a swapped prediction approach to anticipate the representation of the target subgraph from similar instances. Following the pre-training phase, the model is fine-tuned with accurate crowd flow data. Our experiments, conducted on two real-world datasets, demonstrate that the \model pre-trained on extensive noisy data consistently outperforms models trained from scratch.

Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies uniquely determined by the feature map's generator Hamiltonians. Ordinarily, these data-encoding generators are chosen in advance, fixing the space of functions that can be represented. In this work we consider a generalization of quantum models to include a set of trainable parameters in the generator, leading to a trainable frequency (TF) quantum model. We numerically demonstrate how TF models can learn generators with desirable properties for solving the task at hand, including non-regularly spaced frequencies in their spectra and flexible spectral richness. Finally, we showcase the real-world effectiveness of our approach, demonstrating an improved accuracy in solving the Navier-Stokes equations using a TF model with only a single parameter added to each encoding operation. Since TF models encompass conventional fixed frequency models, they may offer a sensible default choice for variational quantum machine learning.

Rational and neural network based approximations are efficient tools in modern approximation. These approaches are able to produce accurate approximations to nonsmooth and non-Lipschitz functions, including multivariate domain functions. In this paper we compare the efficiency of function approximation using rational approximation, neural network and their combinations. It was found that rational approximation is superior to neural network based approaches with the same number of decision variables. Our numerical experiments demonstrate the efficiency of rational approximation, even when the number of approximation parameters (that is, the dimension of the corresponding optimisation problems) is small. Another important contribution of this paper lies in the improvement of rational approximation algorithms. Namely, the optimisation based algorithms for rational approximation can be adjusted to in such a way that the conditioning number of the constraint matrices are controlled. This simple adjustment enables us to work with high dimension optimisation problems and improve the design of the neural network. The main strength of neural networks is in their ability to handle models with a large number of variables: complex models are decomposed in several simple optimisation problems. Therefore the the large number of decision variables is in the nature of neural networks.

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, focusing on evolutionary agents in Gymnasium (game) simulators -- where fitness computation is costly. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update throughout an evolutionary run a fitness-approximation ML model. We compare different methods for: 1) switching between actual and approximate fitness, 2) sampling the population, and 3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than that of the fully run GA -- depending on the ratio of approximate-to-actual-fitness computation. Our approach is generic and can be easily applied to many different domains.

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial topology of the traffic network. In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data. We then use graph convolutional networks to generate traffic forecasts. To enable our method to efficiently model nonlinear traffic propagation patterns, we develop a deep learning-based module as a hyper-network to generate stepwise dynamic causal graphs. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method. The code is available at https://github.com/MonBG/DCGCN.

$ $Acoustic-to-articulatory inversion (AAI) involves mapping from the acoustic space to the articulatory space. Signal-processing features like the MFCCs, have been widely used for the AAI task. For subjects with dysarthric speech, AAI is challenging because of an imprecise and indistinct pronunciation. In this work, we perform AAI for dysarthric speech using representations from pre-trained self-supervised learning (SSL) models. We demonstrate the impact of different pre-trained features on this challenging AAI task, at low-resource conditions. In addition, we also condition x-vectors to the extracted SSL features to train a BLSTM network. In the seen case, we experiment with three AAI training schemes (subject-specific, pooled, and fine-tuned). The results, consistent across training schemes, reveal that DeCoAR, in the fine-tuned scheme, achieves a relative improvement of the Pearson Correlation Coefficient (CC) by ${\sim}$1.81\% and ${\sim}$4.56\% for healthy controls and patients, respectively, over MFCCs. In the unseen case, we observe similar average trends for different SSL features. Overall, SSL networks like wav2vec, APC, and DeCoAR, which are trained with feature reconstruction or future timestep prediction tasks, perform well in predicting dysarthric articulatory trajectories.

Multivariate time series classification is an important computational task arising in applications where data is recorded over time and over multiple channels. For example, a smartwatch can record the acceleration and orientation of a person's motion, and these signals are recorded as multivariate time series. We can classify this data to understand and predict human movement and various properties such as fitness levels. In many applications classification alone is not enough, we often need to classify but also understand what the model learns (e.g., why was a prediction given, based on what information in the data). The main focus of this paper is on analysing and evaluating explanation methods tailored to Multivariate Time Series Classification (MTSC). We focus on saliency-based explanation methods that can point out the most relevant channels and time series points for the classification decision. We analyse two popular and accurate multivariate time series classifiers, ROCKET and dResNet, as well as two popular explanation methods, SHAP and dCAM. We study these methods on 3 synthetic datasets and 2 real-world datasets and provide a quantitative and qualitative analysis of the explanations provided. We find that flattening the multivariate datasets by concatenating the channels works as well as using multivariate classifiers directly and adaptations of SHAP for MTSC work quite well. Additionally, we also find that the popular synthetic datasets we used are not suitable for time series analysis.

Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.

The evolution of Generative AI and the capabilities of the newly released Large Language Models (LLMs) open new opportunities in software engineering. However, they also lead to new challenges in cybersecurity. Recently, researchers have shown the possibilities of using LLMs such as ChatGPT to generate malicious content that can directly be exploited or guide inexperienced hackers to weaponize tools and code. These studies covered scenarios that still require the attacker to be in the middle of the loop. In this study, we leverage openly available plugins and use an LLM as proxy between the attacker and the victim. We deliver a proof-of-concept where ChatGPT is used for the dissemination of malicious software while evading detection, alongside establishing the communication to a command and control (C2) server to receive commands to interact with a victim's system. Finally, we present the general approach as well as essential elements in order to stay undetected and make the attack a success. This proof-of-concept highlights significant cybersecurity issues with openly available plugins and LLMs, which require the development of security guidelines, controls, and mitigation strategies.

Short-term load forecasting is of paramount importance in the efficient operation and planning of power systems, given its inherent non-linear and dynamic nature. Recent strides in deep learning have shown promise in addressing this challenge. However, these methods often grapple with hyperparameter sensitivity, opaqueness in interpretability, and high computational overhead for real-time deployment. In this paper, I propose a novel solution that surmounts these obstacles. Our approach harnesses the power of the Particle-Swarm Optimization algorithm to autonomously explore and optimize hyperparameters, a Multi-Head Attention mechanism to discern the salient features crucial for accurate forecasting, and a streamlined framework for computational efficiency. Our method undergoes rigorous evaluation using a genuine electricity demand dataset. The results underscore its superiority in terms of accuracy, robustness, and computational efficiency. Notably, our Mean Absolute Percentage Error of 1.9376 marks a significant advancement over existing state-of-the-art approaches, heralding a new era in short-term load forecasting.

It is shown that a class of optical physical unclonable functions (PUFs) can be learned to arbitrary precision with arbitrarily high probability, even in the presence of noise, given access to polynomially many challenge-response pairs and polynomially bounded computational power, under mild assumptions about the distributions of the noise and challenge vectors. This extends the results of Rh\"uramir et al. (2013), who showed a subset of this class of PUFs to be learnable in polynomial time in the absence of noise, under the assumption that the optics of the PUF were either linear or had negligible nonlinear effects. We derive polynomial bounds for the required number of samples and the computational complexity of a linear regression algorithm, based on size parameters of the PUF, the distributions of the challenge and noise vectors, and the probability and accuracy of the regression algorithm, with a similar analysis to one done by Bootle et al. (2018), who demonstrated a learning attack on a poorly implemented version of the Learning With Errors problem.

While recommender systems have significantly benefited from implicit feedback, they have often missed the nuances of multi-behavior interactions between users and items. Historically, these systems either amalgamated all behaviors, such as \textit{impression} (formerly \textit{view}), \textit{add-to-cart}, and \textit{buy}, under a singular 'interaction' label, or prioritized only the target behavior, often the \textit{buy} action, discarding valuable auxiliary signals. Although recent advancements tried addressing this simplification, they primarily gravitated towards optimizing the target behavior alone, battling with data scarcity. Additionally, they tended to bypass the nuanced hierarchy intrinsic to behaviors. To bridge these gaps, we introduce the \textbf{H}ierarchical \textbf{M}ulti-behavior \textbf{G}raph Attention \textbf{N}etwork (HMGN). This pioneering framework leverages attention mechanisms to discern information from both inter and intra-behaviors while employing a multi-task Hierarchical Bayesian Personalized Ranking (HBPR) for optimization. Recognizing the need for scalability, our approach integrates a specialized multi-behavior sub-graph sampling technique. Moreover, the adaptability of HMGN allows for the seamless inclusion of knowledge metadata and time-series data. Empirical results attest to our model's prowess, registering a notable performance boost of up to 64\% in NDCG@100 metrics over conventional graph neural network methods.

Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values. Target Networks are employed to stabilize training by using an additional set of lagging parameters to estimate the target values. Despite the popularity of Target Networks, their effect on the optimization is still misunderstood. In this work, we show that they act as an implicit regularizer which can be beneficial in some cases, but also have disadvantages such as being inflexible and can result in instabilities, even when vanilla TD(0) converges. To overcome these issues, we propose an explicit Functional Regularization alternative that is flexible and a convex regularizer in function space and we theoretically study its convergence. We conduct an experimental study across a range of environments, discount factors, and off-policiness data collections to investigate the effectiveness of the regularization induced by Target Networks and Functional Regularization in terms of performance, accuracy, and stability. Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement. Furthermore, adjusting both the regularization weight and the network update period in Functional Regularization can result in further performance improvements compared to solely adjusting the network update period as typically done with Target Networks. Our approach also enhances the ability to networks to recover accurate $Q$-values.

This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has $p$-th moment with $p \in (1,2)$. Instead of using estimation procedure based on truncated observed data, we choose the optimizer by minimizing the risk value. Those risk values can be robustly estimated via using the remarkable Catoni's method (Catoni, 2012). Thanks to the structure of Catoni-type influence functions, we are able to establish excess risk upper bounds via using generalized generic chaining methods. Moreover, we take computational issues into consideration. We especially theoretically investigate two types of optimization methods, robust gradient descent algorithm and empirical risk-based methods. With an extensive numerical study, we find that the optimizer based on empirical risks via Catoni-style estimation indeed shows better performance than other baselines. It indicates that estimation directly based on truncated data may lead to unsatisfactory results.

Consider two forecasters, each making a single prediction for a sequence of events over time. We ask a relatively basic question: how might we compare these forecasters, either online or post-hoc, while avoiding unverifiable assumptions on how the forecasts and outcomes were generated? In this paper, we present a rigorous answer to this question by designing novel sequential inference procedures for estimating the time-varying difference in forecast scores. To do this, we employ confidence sequences (CS), which are sequences of confidence intervals that can be continuously monitored and are valid at arbitrary data-dependent stopping times ("anytime-valid"). The widths of our CSs are adaptive to the underlying variance of the score differences. Underlying their construction is a game-theoretic statistical framework, in which we further identify e-processes and p-processes for sequentially testing a weak null hypothesis -- whether one forecaster outperforms another on average (rather than always). Our methods do not make distributional assumptions on the forecasts or outcomes; our main theorems apply to any bounded scores, and we later provide alternative methods for unbounded scores. We empirically validate our approaches by comparing real-world baseball and weather forecasters.

In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and adversarial defenses. However, the use of transformed data decreases the performance of models. Accordingly, in this paper, we propose a novel method for fine-tuning models with transformed images under the use of the vision transformer (ViT). The proposed domain adaptation method does not cause the accuracy degradation of models, and it is carried out on the basis of the embedding structure of ViT. In experiments, we confirmed that the proposed method prevents accuracy degradation even when using encrypted images with the CIFAR-10 and CIFAR-100 datasets.

Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. However, this procedure is a priori factorially costly to implement, making it impractical for large numbers of particles. The strategy also suffers from a sign problem. Namely, due to near-exact cancellation of positive and negative contributions, the magnitude of the antisymmetrized function may be significantly smaller than before anti-symmetrization. We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes. This approximation is effective when the sign problem is controlled, and we show that this property depends crucially the choice of activation function under standard Xavier/He initialization methods. As a consequence, using a smooth activation function requires re-scaling of the neural network weights compared to standard initializations.

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks. To this end, \textit{unlearnable examples} (UEs) have been recently proposed as a compelling protection, by adding imperceptible perturbation to data so that models trained on them cannot classify them accurately on original clean distribution. Unfortunately, we find UEs provide a false sense of security, because they cannot stop unauthorized users from utilizing other unprotected data to remove the protection, by turning unlearnable data into learnable again. Motivated by this observation, we formally define a new threat by introducing \textit{learnable unauthorized examples} (LEs) which are UEs with their protection removed. The core of this approach is a novel purification process that projects UEs onto the manifold of LEs. This is realized by a new joint-conditional diffusion model which denoises UEs conditioned on the pixel and perceptual similarity between UEs and LEs. Extensive experiments demonstrate that LE delivers state-of-the-art countering performance against both supervised UEs and unsupervised UEs in various scenarios, which is the first generalizable countermeasure to UEs across supervised learning and unsupervised learning. Our code is available at \url{https://github.com/jiangw-0/LE_JCDP}.

The burgeoning growth of public domain data and the increasing complexity of deep learning model architectures have underscored the need for more efficient data representation and analysis techniques. This paper is motivated by the work of Helal (2023) and aims to present a comprehensive overview of tensorization. This transformative approach bridges the gap between the inherently multidimensional nature of data and the simplified 2-dimensional matrices commonly used in linear algebra-based machine learning algorithms. This paper explores the steps involved in tensorization, multidimensional data sources, various multiway analysis methods employed, and the benefits of these approaches. A small example of Blind Source Separation (BSS) is presented comparing 2-dimensional algorithms and a multiway algorithm in Python. Results indicate that multiway analysis is more expressive. Contrary to the intuition of the dimensionality curse, utilising multidimensional datasets in their native form and applying multiway analysis methods grounded in multilinear algebra reveal a profound capacity to capture intricate interrelationships among various dimensions while, surprisingly, reducing the number of model parameters and accelerating processing. A survey of the multi-away analysis methods and integration with various Deep Neural Networks models is presented using case studies in different domains.

We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.

Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics fail to measure the impact of this mismatch, especially for clinical data sets that include low signal pathologies, a difficult segmentation task, and uncertain, small, or empty reference annotations. This limitation may result in ineffective research of machine learning practitioners in designing and optimizing models. Dimensions of evaluating clinical value include consideration of the uncertainty of reference annotations, independence from reference annotation volume size, and evaluation of classification of empty reference annotations. We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify metrics with clinical value. We compare to a public benchmark data set (BraTS 2019) with a high-signal pathology and certain, larger, and no empty reference annotations. We may show machine learning practitioners, how uncertain, small, or empty reference annotations require a rethinking of the evaluation and optimizing procedures. The evaluation code was released to encourage further analysis of this topic. https://github.com/SophieOstmeier/UncertainSmallEmpty.git

Learning for Demonstration (LfD) enables robots to acquire new skills by imitating expert demonstrations, allowing users to communicate their instructions in an intuitive manner. Recent progress in LfD often relies on kinesthetic teaching or teleoperation as the medium for users to specify the demonstrations. Kinesthetic teaching requires physical handling of the robot, while teleoperation demands proficiency with additional hardware. This paper introduces an alternative paradigm for LfD called Diagrammatic Teaching. Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space. Additionally, we present the Ray-tracing Probabilistic Trajectory Learning (RPTL) framework for Diagrammatic Teaching. RPTL extracts time-varying probability densities from the 2D sketches, applies ray-tracing to find corresponding regions in 3D Cartesian space, and fits a probabilistic model of motion trajectories to these regions. New motion trajectories, which mimic those sketched by the user, can then be generated from the probabilistic model. We empirically validate our framework both in simulation and on real robots, which include a fixed-base manipulator and a quadruped-mounted manipulator.

Recent studies have verified that equivariant methods can significantly improve the data efficiency, generalizability, and robustness in robot learning. Meanwhile, denoising diffusion-based generative modeling has recently gained significant attention as a promising approach for robotic manipulation learning from demonstrations with stochastic behaviors. In this paper, we present Diffusion-EDFs, a novel approach that incorporates spatial roto-translation equivariance, i.e., SE(3)-equivariance to diffusion generative modeling. By integrating SE(3)-equivariance into our model architectures, we demonstrate that our proposed method exhibits remarkable data efficiency, requiring only 5 to 10 task demonstrations for effective end-to-end training. Furthermore, our approach showcases superior generalizability compared to previous diffusion-based manipulation methods.

As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.

Instruction-tuned Large Language Models (LLMs) have recently showcased remarkable ability to generate fitting responses to natural language instructions. However, an open research question concerns the inherent biases of trained models and their responses. For instance, if the data used to tune an LLM is dominantly written by persons with a specific political bias, we might expect generated answers to share this bias. Current research work seeks to de-bias such models, or suppress potentially biased answers. With this demonstration, we take a different view on biases in instruction-tuning: Rather than aiming to suppress them, we aim to make them explicit and transparent. To this end, we present OpinionGPT, a web demo in which users can ask questions and select all biases they wish to investigate. The demo will answer this question using a model fine-tuned on text representing each of the selected biases, allowing side-by-side comparison. To train the underlying model, we identified 11 different biases (political, geographic, gender, age) and derived an instruction-tuning corpus in which each answer was written by members of one of these demographics. This paper presents OpinionGPT, illustrates how we trained the bias-aware model and showcases the web application (available at https://opiniongpt.informatik.hu-berlin.de).

We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tuning, different from which, our ImageBind-LLM can respond to multi-modality conditions, including audio, 3D point clouds, video, and their embedding-space arithmetic by only image-text alignment training. During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder. Then, the image features transformed by the bind network are added to word tokens of all layers in LLaMA, which progressively injects visual instructions via an attention-free and zero-initialized gating mechanism. Aided by the joint embedding of ImageBind, the simple image-text training enables our model to exhibit superior multi-modality instruction-following capabilities. During inference, the multi-modality inputs are fed into the corresponding ImageBind encoders, and processed by a proposed visual cache model for further cross-modal embedding enhancement. The training-free cache model retrieves from three million image features extracted by ImageBind, which effectively mitigates the training-inference modality discrepancy. Notably, with our approach, ImageBind-LLM can respond to instructions of diverse modalities and demonstrate significant language generation quality. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter.

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal-dual contextual Bayesian optimization algorithm is proposed that achieves sublinear cumulative regret with respect to the dynamic optimal solution under certain regularity conditions. Furthermore, the algorithm achieves zero time-average constraint violation, ensuring that the average value of the constraint function satisfies the desired constraint. The method is applied to both sampled instances from Gaussian processes and a continuous stirred tank reactor parameter tuning problem; simulation results show that the method simultaneously provides close-to-optimal performance and maintains constraint feasibility on average. This contrasts current state-of-the-art methods, which either suffer from large cumulative regret or severe constraint violations for the case studies presented.

Reinforcement learning algorithms often require finiteness of state and action spaces in Markov decision processes (MDPs) (also called controlled Markov chains) and various efforts have been made in the literature towards the applicability of such algorithms for continuous state and action spaces. In this paper, we show that under very mild regularity conditions (in particular, involving only weak continuity of the transition kernel of an MDP), Q-learning for standard Borel MDPs via quantization of states and actions (called Quantized Q-Learning) converges to a limit, and furthermore this limit satisfies an optimality equation which leads to near optimality with either explicit performance bounds or which are guaranteed to be asymptotically optimal. Our approach builds on (i) viewing quantization as a measurement kernel and thus a quantized MDP as a partially observed Markov decision process (POMDP), (ii) utilizing near optimality and convergence results of Q-learning for POMDPs, and (iii) finally, near-optimality of finite state model approximations for MDPs with weakly continuous kernels which we show to correspond to the fixed point of the constructed POMDP. Thus, our paper presents a very general convergence and approximation result for the applicability of Q-learning for continuous MDPs.

Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools. This paper aims to challenge this misconception. With sufficient training data, a 2 billion-parameter language model can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem test set.

Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption. To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user. Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front. In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization. Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator. Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator. Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach. In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.

The Lipschitz bound, a technique from robust statistics, can limit the maximum changes in the output concerning the input, taking into account associated irrelevant biased factors. It is an efficient and provable method for examining the output stability of machine learning models without incurring additional computation costs. Recently, Graph Neural Networks (GNNs), which operate on non-Euclidean data, have gained significant attention. However, no previous research has investigated the GNN Lipschitz bounds to shed light on stabilizing model outputs, especially when working on non-Euclidean data with inherent biases. Given the inherent biases in common graph data used for GNN training, it poses a serious challenge to constraining the GNN output perturbations induced by input biases, thereby safeguarding fairness during training. Recently, despite the Lipschitz constant's use in controlling the stability of Euclideanneural networks, the calculation of the precise Lipschitz constant remains elusive for non-Euclidean neural networks like GNNs, especially within fairness contexts. To narrow this gap, we begin with the general GNNs operating on an attributed graph, and formulate a Lipschitz bound to limit the changes in the output regarding biases associated with the input. Additionally, we theoretically analyze how the Lipschitz constant of a GNN model could constrain the output perturbations induced by biases learned from data for fairness training. We experimentally validate the Lipschitz bound's effectiveness in limiting biases of the model output. Finally, from a training dynamics perspective, we demonstrate why the theoretical Lipschitz bound can effectively guide the GNN training to better trade-off between accuracy and fairness.

Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.

The choice of the objective function is crucial in emerging high-quality representations from self-supervised learning. This paper investigates how different formulations of the Barlow Twins (BT) objective impact downstream task performance for speech data. We propose Modified Barlow Twins (MBT) with normalized latents to enforce scale-invariance and evaluate on speaker identification, gender recognition and keyword spotting tasks. Our results show MBT improves representation generalization over original BT, especially when fine-tuning with limited target data. This highlights the importance of designing objectives that encourage invariant and transferable representations. Our analysis provides insights into how the BT learning objective can be tailored to produce speech representations that excel when adapted to new downstream tasks. This study is an important step towards developing reusable self-supervised speech representations.

Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities. However, in times of threats to its supply, it is also a critical element that guarantees the supply of this raw material to meet individual consumers' needs, ensuring society's energy security. This article introduces a novel multistep ahead forecasting of natural gas consumption with change point detection integration for model collection selection with continual learning capabilities using data stream processing. The performance of the forecasting models based on the proposed approach is evaluated in a complex real-world use case of natural gas consumption forecasting. We employed Hoeffding tree predictors as forecasting models and the Pruned Exact Linear Time (PELT) algorithm for the change point detection procedure. The change point detection integration enables selecting a different model collection for successive time frames. Thus, three model collection selection procedures (with and without an error feedback loop) are defined and evaluated for forecasting scenarios with various densities of detected change points. These models were compared with change point agnostic baseline approaches. Our experiments show that fewer change points result in a lower forecasting error regardless of the model collection selection procedure employed. Also, simpler model collection selection procedures omitting forecasting error feedback leads to more robust forecasting models suitable for continual learning tasks.

Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.

To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3ml, and 3mm. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46+-0.09 Surface Dice at Tolerance 5mm and 0.47+-0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, p < 0.05. The CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.

This paper presents a novel deep learning approach for analyzing massive underwater acoustic data by leveraging a model trained on a broad spectrum of non-underwater (aerial) sounds. Recognizing the challenge in labeling vast amounts of underwater data, we propose a two-fold methodology to accelerate this labor-intensive procedure. The first part of our approach involves PCA and UMAP visualization of the underwater data using the feature vectors of an aerial sound recognition model. This enables us to cluster the data in a two dimensional space and listen to points within these clusters to understand their defining characteristics. This innovative method simplifies the process of selecting candidate labels for further training. In the second part, we train a neural network model using both the selected underwater data and the non-underwater dataset. We conducted a quantitative analysis to measure the precision, recall, and F1 score of our model for recognizing airgun sounds, a common type of underwater sound. The F1 score achieved by our model exceeded 84.3%, demonstrating the effectiveness of our approach in analyzing underwater acoustic data. The methodology presented in this paper holds significant potential to reduce the amount of labor required in underwater data analysis and opens up new possibilities for further research in the field of cross-domain data analysis.

This paper examines some common problems in Human-Robot Interaction (HRI) causing failures and troubles in Chat. A given use case's design decisions start with the suitable robot, the suitable chatting model, identifying common problems that cause failures, identifying potential solutions, and planning continuous improvement. In conclusion, it is recommended to use a closed-loop control algorithm that guides the use of trained Artificial Intelligence (AI) pre-trained models and provides vocabulary filtering, re-train batched models on new datasets, learn online from data streams, and/or use reinforcement learning models to self-update the trained models and reduce errors.

Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine Learning (ML) assisted solutions are a promising answer. We argue that a complexity-reduced problem description and the data representing it, will facilitate the solution exploration workflow. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo. REDVID is intended as a simulation-in-the-loop, to both generate synthetic data efficiently and to simplify the challenge of ML model design. The fully parametric nature of our tool, with regards to system-level configuration, while in contrast to physics-accurate simulations, allows for the generation of simplified data for research and education, at different levels. Resulting from the reduced complexity, we showcase the computational efficiency of REDVID by providing the computational cost figures for a multitude of simulation benchmarks. As a simulation and a generative tool for ML-assisted solution design, REDVID is highly flexible, reusable and open-source. Reference data sets generated with REDVID are publicly available.

Given pairwise comparisons between multiple items, how to rank them so that the ranking matches the observations? This problem, known as rank aggregation, has found many applications in sports, recommendation systems, and other web applications. As it is generally NP-hard to find a global ranking that minimizes the mismatch (known as the Kemeny optimization), we focus on the Erd\"os-R\'enyi outliers (ERO) model for this ranking problem. Here, each pairwise comparison is a corrupted copy of the true score difference. We investigate spectral ranking algorithms that are based on unnormalized and normalized data matrices. The key is to understand their performance in recovering the underlying scores of each item from the observed data. This reduces to deriving an entry-wise perturbation error bound between the top eigenvectors of the unnormalized/normalized data matrix and its population counterpart. By using the leave-one-out technique, we provide a sharper $\ell_{\infty}$-norm perturbation bound of the eigenvectors and also derive an error bound on the maximum displacement for each item, with only $\Omega(n\log n)$ samples. Our theoretical analysis improves upon the state-of-the-art results in terms of sample complexity, and our numerical experiments confirm these theoretical findings.

Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its performance. Therefore, maintaining a constant monitoring process for machine learning model performance is crucial in order to proactively prevent any potential performance regression. However, supervised drift detection methods require human annotation and consequently lead to a longer time to detect and mitigate the drift. In our proposed unsupervised drift detection method, we follow a two step process. Our first step involves encoding a sample of production data as the target distribution, and the model training data as the reference distribution. In the second step, we employ a kernel-based statistical test that utilizes the maximum mean discrepancy (MMD) distance metric to compare the reference and target distributions and estimate any potential drift. Our method also identifies the subset of production data that is the root cause of the drift. The models retrained using these identified high drift samples show improved performance on online customer experience quality metrics.

Current machine learning methods struggle to solve Bongard problems, which are a type of IQ test that requires deriving an abstract "concept" from a set of positive and negative "support" images, and then classifying whether or not a new query image depicts the key concept. On Bongard-HOI, a benchmark for natural-image Bongard problems, existing methods have only reached 66% accuracy (where chance is 50%). Low accuracy is often attributed to neural nets' lack of ability to find human-like symbolic rules. In this work, we point out that many existing methods are forfeiting accuracy due to a much simpler problem: they do not incorporate information contained in the support set as a whole, and rely instead on information extracted from individual supports. This is a critical issue, because unlike in few-shot learning tasks concerning object classification, the "key concept" in a typical Bongard problem can only be distinguished using multiple positives and multiple negatives. We explore a variety of simple methods to take this cross-image context into account, and demonstrate substantial gains over prior methods, leading to new state-of-the-art performance on Bongard-LOGO (75.3%) and Bongard-HOI (72.45%) and strong performance on the original Bongard problem set (60.84%).

In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making. Typically, such a policy must be derived from observational data, which introduces several challenges. While the problem of ``endogeneity'' is prominently studied in the established pricing literature, the problem of selection bias (or, more precisely, bid selection bias) is not. We take a step towards understanding the effects of selection bias by posing pricing as a problem of causal inference. Specifically, we consider the reaction of a customer to price a treatment effect. In our experiments, we simulate varying levels of selection bias on a semi-synthetic dataset on mortgage loan applications in Belgium. We investigate the potential of parametric and nonparametric methods for the identification of individual bid-response functions. Our results illustrate how conventional methods such as logistic regression and neural networks suffer adversely from selection bias. In contrast, we implement state-of-the-art methods from causal machine learning and show their capability to overcome selection bias in pricing data.

We present a data-driven model for ground-motion synthesis using a Generative Adversarial Neural Operator (GANO) that combines recent advancements in machine learning and open access strong motion data sets to generate three-component acceleration time histories conditioned on moment magnitude ($M$), rupture distance ($R_{rup}$), time-average shear-wave velocity at the top $30m$ ($V_{S30}$), and tectonic environment or style of faulting. We use Neural Operators, a resolution invariant architecture that guarantees that the model training is independent of the data sampling frequency. We first present the conditional ground-motion synthesis algorithm (referred to heretofore as cGM-GANO) and discuss its advantages compared to previous work. Next, we verify the cGM-GANO framework using simulated ground motions generated with the Southern California Earthquake Center (SCEC) Broadband Platform (BBP). We lastly train cGM-GANO on a KiK-net dataset from Japan, showing that the framework can recover the magnitude, distance, and $V_{S30}$ scaling of Fourier amplitude and pseudo-spectral accelerations. We evaluate cGM-GANO through residual analysis with the empirical dataset as well as by comparison with conventional Ground Motion Models (GMMs) for selected ground motion scenarios. Results show that cGM-GANO produces consistent median scaling with the GMMs for the corresponding tectonic environments. The largest misfit is observed at short distances due to the scarcity of training data. With the exception of short distances, the aleatory variability of the response spectral ordinates is also well captured, especially for subduction events due to the adequacy of training data. Applications of the presented framework include generation of risk-targeted ground motions for site-specific engineering applications.

Existing approaches for classifying dynamic graphs either lift graph kernels to the temporal domain, or use graph neural networks (GNNs). However, current baselines have scalability issues, cannot handle a changing node set, or do not take edge weight information into account. We propose filtration surfaces, a novel method that is scalable and flexible, to alleviate said restrictions. We experimentally validate the efficacy of our model and show that filtration surfaces outperform previous state-of-the-art baselines on datasets that rely on edge weight information. Our method does so while being either completely parameter-free or having at most one parameter, and yielding the lowest overall standard deviation.

We study distribution testing in the standard access model and the conditional access model when the memory available to the testing algorithm is bounded. In both scenarios, the samples appear in an online fashion and the goal is to test the properties of distribution using an optimal number of samples subject to a memory constraint on how many samples can be stored at a given time. First, we provide a trade-off between the sample complexity and the space complexity for testing identity when the samples are drawn according to the conditional access oracle. We then show that we can learn a succinct representation of a monotone distribution efficiently with a memory constraint on the number of samples that are stored that is almost optimal. We also show that the algorithm for monotone distributions can be extended to a larger class of decomposable distributions.

This work proposes a robust Partial Domain Adaptation (PDA) framework that mitigates the negative transfer problem by incorporating a robust target-supervision strategy. It leverages ensemble learning and includes diverse, complementary label feedback, alleviating the effect of incorrect feedback and promoting pseudo-label refinement. Rather than relying exclusively on first-order moments for distribution alignment, our approach offers explicit objectives to optimize intra-class compactness and inter-class separation with the inferred source prototypes and highly-confident target samples in a domain-invariant fashion. Notably, we ensure source data privacy by eliminating the need to access the source data during the adaptation phase through a priori inference of source prototypes. We conducted a series of comprehensive experiments, including an ablation analysis, covering a range of partial domain adaptation tasks. Comprehensive evaluations on benchmark datasets corroborate our framework's enhanced robustness and generalization, demonstrating its superiority over existing state-of-the-art PDA approaches.

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners - KG Builders, Analysts, and Consumers - each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multi-residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

This paper proposes a novel approach for detecting objects using mobile robots in the context of the RoboCup Standard Platform League, with a primary focus on detecting the ball. The challenge lies in detecting a dynamic object in varying lighting conditions and blurred images caused by fast movements. To address this challenge, the paper presents a convolutional neural network architecture designed specifically for computationally constrained robotic platforms. The proposed CNN is trained to achieve high precision classification of single objects in image patches and to determine their precise spatial positions. The paper further integrates Early Exits into the existing high-precision CNN architecture to reduce the computational cost of easily rejectable cases in the background class. The training process involves a composite loss function based on confidence and positional losses with dynamic weighting and data augmentation. The proposed approach achieves a precision of 100% on the validation dataset and a recall of almost 87%, while maintaining an execution time of around 170 $\mu$s per hypotheses. By combining the proposed approach with an Early Exit, a runtime optimization of more than 28%, on average, can be achieved compared to the original CNN. Overall, this paper provides an efficient solution for an enhanced detection of objects, especially the ball, in computationally constrained robotic platforms.

Source camera identification in digital videos is the problem of associating an unknown digital video with its source device, within a closed set of possible devices. The existing techniques in source detection of digital videos try to find a fingerprint of the actual source in the video in form of PRNU (Photo Response Non--Uniformity), and match it against the SPN (Sensor Pattern Noise) of each possible device. The highest correlation indicates the correct source. We investigate the problem of identifying a video source through a feature based approach using machine learning. In this paper, we present a blind forensic technique of video source authentication and identification, based on feature extraction, feature selection and subsequent source classification. The main aim is to determine whether a claimed source for a video is actually its original source. If not, we identify its original source. Our experimental results prove the efficiency of the proposed method compared to traditional fingerprint based technique.

Federated learning (FL) is designed to preserve data privacy during model training, where the data remains on the client side (i.e., IoT devices), and only model updates of clients are shared iteratively for collaborative learning. However, this process is vulnerable to privacy attacks and Byzantine attacks: the local model updates shared throughout the FL network will leak private information about the local training data, and they can also be maliciously crafted by Byzantine attackers to disturb the learning. In this paper, we propose a new FL scheme that guarantees rigorous privacy and simultaneously enhances system robustness against Byzantine attacks. Our approach introduces sparsification- and momentum-driven variance reduction into the client-level differential privacy (DP) mechanism, to defend against Byzantine attackers. The security design does not violate the privacy guarantee of the client-level DP mechanism; hence, our approach achieves the same client-level DP guarantee as the state-of-the-art. We conduct extensive experiments on both IID and non-IID datasets and different tasks and evaluate the performance of our approach against different Byzantine attacks by comparing it with state-of-the-art defense methods. The results of our experiments show the efficacy of our framework and demonstrate its ability to improve system robustness against Byzantine attacks while achieving a strong privacy guarantee.

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow. In response to this need, we introduce Auto Bioinformatics Analysis (AutoBA), an autonomous AI agent based on a large language model designed explicitly for conventional omics data analysis. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBA's robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome sequencing (WGS), RNA sequencing (RNA-seq), single-cell RNA-seq, ChIP-seq, and spatial transcriptomics. AutoBA's unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA deploys the analysis locally, preserving data privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents a convenient tool, offering robustness and adaptability for complex omics data analysis.

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides an instance space analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best when given the characteristics of a sports timetabling problem instance. Furthermore, we identify which characteristics are important in making that prediction, providing insights in the performance of the algorithms, and suggestions to further improve them. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.

Surveillance systems have emerged as crucial elements in upholding peace and security in the modern world. Their ubiquity aids in monitoring suspicious activities effectively. However, in densely populated environments, continuous active monitoring becomes impractical, necessitating the development of intelligent surveillance systems. AI integration in the surveillance domain was a big revolution, however, speed issues have prevented its widespread implementation in the field. It has been observed that quantum artificial intelligence has led to a great breakthrough. Quantum artificial intelligence-based surveillance systems have shown to be more accurate as well as capable of performing well in real-time scenarios, which had never been seen before. In this research, a RentinaNet model is integrated with Quantum CNN and termed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN, Quantum-RetinaNet strikes a balance between accuracy and speed. This innovative integration positions it as a game-changer, addressing the challenges of active monitoring in densely populated scenarios. As demand for efficient surveillance solutions continues to grow, Quantum-RetinaNet offers a compelling alternative to existing CNN models, upholding accuracy standards without sacrificing real-time performance. The unique attributes of Quantum-RetinaNet have far-reaching implications for the future of intelligent surveillance. With its enhanced processing speed, it is poised to revolutionize the field, catering to the pressing need for rapid yet precise monitoring. As Quantum-RetinaNet becomes the new standard, it ensures public safety and security while pushing the boundaries of AI in surveillance.

Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation. Through the lens of discrete cosine transformation, we find the main reason is that \emph{the same noise level on a higher resolution results in a higher Signal-to-Noise Ratio in the frequency domain}. In this work, we present Relay Diffusion Model (RDM), which transfers a low-resolution image or noise into an equivalent high-resolution one for diffusion model via blurring diffusion and block noise. Therefore, the diffusion process can continue seamlessly in any new resolution or model without restarting from pure noise or low-resolution conditioning. RDM achieves state-of-the-art FID on CelebA-HQ and sFID on ImageNet 256$\times$256, surpassing previous works such as ADM, LDM and DiT by a large margin. All the codes and checkpoints are open-sourced at \url{https://github.com/THUDM/RelayDiffusion}.

We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce a mode orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm that is guaranteed to converge to a stationary point. By learning unique and common representations across datasets, we demonstrate perTucker's effectiveness in anomaly detection, client classification, and clustering through a simulation study and two case studies on solar flare detection and tonnage signal classification.

Joint Embedding Architecture-based self-supervised learning methods have attributed the composition of data augmentations as a crucial factor for their strong representation learning capabilities. While regional dropout strategies have proven to guide models to focus on lesser indicative parts of the objects in supervised methods, it hasn't been adopted by self-supervised methods for generating positive pairs. This is because the regional dropout methods are not suitable for the input sampling process of the self-supervised methodology. Whereas dropping informative pixels from the positive pairs can result in inefficient training, replacing patches of a specific object with a different one can steer the model from maximizing the agreement between different positive pairs. Moreover, joint embedding representation learning methods have not made robustness their primary training outcome. To this end, we propose the ViewMix augmentation policy, specially designed for self-supervised learning, upon generating different views of the same image, patches are cut and pasted from one view to another. By leveraging the different views created by this augmentation strategy, multiple joint embedding-based self-supervised methodologies obtained better localization capability and consistently outperformed their corresponding baseline methods. It is also demonstrated that incorporating ViewMix augmentation policy promotes robustness of the representations in the state-of-the-art methods. Furthermore, our experimentation and analysis of compute times suggest that ViewMix augmentation doesn't introduce any additional overhead compared to other counterparts.

Knowledge Distillation (KD) is one proposed solution to large model sizes and slow inference speed in semantic segmentation. In our research we identify 25 proposed distillation loss terms from 14 publications in the last 4 years. Unfortunately, a comparison of terms based on published results is often impossible, because of differences in training configurations. A good illustration of this problem is the comparison of two publications from 2022. Using the same models and dataset, Structural and Statistical Texture Distillation (SSTKD) reports an increase of student mIoU of 4.54 and a final performance of 29.19, while Adaptive Perspective Distillation (APD) only improves student performance by 2.06 percentage points, but achieves a final performance of 39.25. The reason for such extreme differences is often a suboptimal choice of hyperparameters and a resulting underperformance of the student model used as reference point. In our work, we reveal problems of insufficient hyperparameter tuning by showing that distillation improvements of two widely accepted frameworks, SKD and IFVD, vanish when hyperparameters are optimized sufficiently. To improve comparability of future research in the field, we establish a solid baseline for three datasets and two student models and provide extensive information on hyperparameter tuning. We find that only two out of eight techniques can compete with our simple baseline on the ADE20K dataset.

Multimodal models have gained significant success in recent years. Standard multimodal approaches often assume unchanged modalities from training stage to inference stage. In practice, however, many scenarios fail to satisfy such assumptions with missing modalities during inference, leading to limitations on where multimodal models can be applied. While existing methods mitigate the problem through reconstructing the missing modalities, it increases unnecessary computational cost, which could be just as critical, especially for large, deployed systems. To solve the problem from both sides, we propose a novel guidance network that promotes knowledge sharing during training, taking advantage of the multimodal representations to train better single-modality models for inference. Real-life experiment in violence detection shows that our proposed framework trains single-modality models that significantly outperform its traditionally trained counterparts while maintaining the same inference cost.

We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0.

Large language models (LLMs) exhibit impressive language understanding and in-context learning abilities including natural language processing (NLP) tasks and challenging mathematical reasoning. However, due to the lack of process-supervision, applying PLMs to mathematical reasoning tasks often fail to generate correct reasoning steps and final answer even though solutions have high probabilities. To unleash the mathematical reasoning of finetuned-LLMs without any further fineutuning steps, we propose a method to endow LLMs with immediate reaction and delicate reasoning system via Monte Carlo Tree Search(MCTS) and a light energy function to rank the decision steps. In particular, We first re-formalize the finetuned-LLMs to a Residual-based Energy Model~(Residual-EBM) and apply noise contrastive estimation to estimate the parameters of energy function . Then we use MCTS with energy function as path verifier to search the output space and evaluating the reasoning path. Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our method that improve the pass@1 of the finetuned-model without further finetuning or RLHF alignment by a substantial margin.

We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks. We consider many issues that have not been adequately considered before: whether learning over data sets that do not have diverse sets of images affects the results; whether to use a pre-trained feature extraction "backbone"; how to evaluate learner performance (we argue that classification accuracy is not enough), among others. Overall, across a wide variety of settings, we find that vertically decomposing a neural network seems to give the best results, and outperforms more standard reconciliation-used methods.

The analysis of Synthetic Aperture Radar (SAR) imagery is an important step in remote sensing applications, and it is a challenging problem due to its inherent speckle noise. One typical solution is to model the data using the $G_I^0$ distribution and extract its roughness information, which in turn can be used in posterior imaging tasks, such as segmentation, classification and interpretation. This leads to the need of quick and reliable estimation of the roughness parameter from SAR data, especially with high resolution images. Unfortunately, traditional parameter estimation procedures are slow and prone to estimation failures. In this work, we proposed a neural network-based estimation framework that first learns how to predict underlying parameters of $G_I^0$ samples and then can be used to estimate the roughness of unseen data. We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures than the traditional estimation procedures for this problem, even when we use a simple network. More importantly, we show that this same methodology can be generalized to handle image inputs and, even if trained on purely synthetic data for a few seconds, is able to perform real time pixel-wise roughness estimation for high resolution real SAR imagery.

Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels. However, in practice, we do not have at our disposal all the labels associated to the observations to estimate the parameters of such models. In this paper, we propose a general framework based on a variational Bayesian inference to train parameterized triplet Markov chain models in a semi-supervised context. The generality of our approach enables us to derive semi-supervised algorithms for a variety of generative models for sequential Bayesian classification.

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made in this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining a holistic view of performance capabilities. (2) The use of specialized synthetic and private datasets introduces biases and hampers generalizability. (3) Ambiguous evaluation measures, often tied to custom networks or downstream tasks, hinder consistent and fair comparison. To overcome these limitations, we introduce \textsf{TSGBench}, the inaugural TSG Benchmark, designed for a unified and comprehensive assessment of TSG methods. It comprises three modules: (1) a curated collection of publicly available, real-world datasets tailored for TSG, together with a standardized preprocessing pipeline; (2) a comprehensive evaluation measures suite including vanilla measures, new distance-based assessments, and visualization tools; (3) a pioneering generalization test rooted in Domain Adaptation (DA), compatible with all methods. We have conducted extensive experiments across ten real-world datasets from diverse domains, utilizing ten advanced TSG methods and twelve evaluation measures, all gauged through \textsf{TSGBench}. The results highlight its remarkable efficacy and consistency. More importantly, \textsf{TSGBench} delivers a statistical breakdown of method rankings, illuminating performance variations across different datasets and measures, and offering nuanced insights into the effectiveness of each method.

We introduce EGIC, a novel generative image compression method that allows traversing the distortion-perception curve efficiently from a single model. Specifically, we propose an implicitly encoded variant of image interpolation that predicts the residual between a MSE-optimized and GAN-optimized decoder output. On the receiver side, the user can then control the impact of the residual on the GAN-based reconstruction. Together with improved GAN-based building blocks, EGIC outperforms a wide-variety of perception-oriented and distortion-oriented baselines, including HiFiC, MRIC and DIRAC, while performing almost on par with VTM-20.0 on the distortion end. EGIC is simple to implement, very lightweight (e.g. 0.18x model parameters compared to HiFiC) and provides excellent interpolation characteristics, which makes it a promising candidate for practical applications targeting the low bit range.

Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from www.github.com/BoXHED.

The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and qualities. The diversity of data often comes from the use of various scanners of vendors. But, in practice, it is impractical to collect a sufficient amount of diverse data for training. To this end, a novel contrastive learning is developed to equip the deep learning models with better style generalization capability. Specifically, the multi-style and multi-view unsupervised self-learning scheme is carried out to seek robust feature embedding against style diversity as a pretrained model. Afterward, the pretrained network is further fine-tuned to the downstream tasks, e.g., mass detection, matching, BI-RADS rating, and breast density classification. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets. The experimental results suggest that the proposed domain generalization method can effectively improve performance of four mammographic image tasks on the data from both seen and unseen domains, and outperform many state-of-the-art (SOTA) generalization methods.

We introduce the $ARMOR_D$ methods as novel approaches to enhancing the adversarial robustness of deep learning models. These methods are based on a new class of optimal-transport-regularized divergences, constructed via an infimal convolution between an information divergence and an optimal-transport (OT) cost. We use these as tools to enhance adversarial robustness by maximizing the expected loss over a neighborhood of distributions, a technique known as distributionally robust optimization. Viewed as a tool for constructing adversarial samples, our method allows samples to be both transported, according to the OT cost, and re-weighted, according to the information divergence. We demonstrate the effectiveness of our method on malware detection and image recognition applications and find that, to our knowledge, it outperforms existing methods at enhancing the robustness against adversarial attacks. $ARMOR_D$ yields the robustified accuracy of $98.29\%$ against $FGSM$ and $98.18\%$ against $PGD^{40}$ on the MNIST dataset, reducing the error rate by more than $19.7\%$ and $37.2\%$ respectively compared to prior methods. Similarly, in malware detection, a discrete (binary) data domain, $ARMOR_D$ improves the robustified accuracy under $rFGSM^{50}$ attack compared to the previous best-performing adversarial training methods by $37.0\%$ while lowering false negative and false positive rates by $51.1\%$ and $57.53\%$, respectively.

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner.

Estimating the effects of treatments with an associated dose on an instance's outcome, the "dose response", is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such effects, also known as continuous-valued treatment effects, are typically estimated from observational data, which may be subject to dose selection bias. This means that the allocation of doses depends on pre-treatment covariates. Previous studies have shown that conventional machine learning approaches fail to learn accurate individual estimates of dose responses under the presence of dose selection bias. In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data. CBRNet adopts the Neyman-Rubin potential outcome framework and extends the concept of balanced representation learning for overcoming selection bias to continuous-valued treatments. Our work is the first to apply representation balancing in a continuous-valued treatment setting. We evaluate our method on a newly proposed benchmark. Our experiments demonstrate CBRNet's ability to accurately learn treatment effects under selection bias and competitive performance with respect to other state-of-the-art methods.

Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection. The proposed layerwise propagation rule of our model is theoretically motivated by the concept of implicit fairing in geometry processing, and comprises a graph convolution module for aggregating information from immediate node neighbors and a skip connection module for combining layer-wise neighborhood representations. This propagation rule is derived from the iterative solution of the implicit fairing equation via the Jacobi method. In addition to capturing information from distant graph nodes through skip connections between the network's layers, our approach exploits both the graph structure and node features for learning discriminative node representations. These skip connections are integrated by design in our proposed network architecture. The effectiveness of our model is demonstrated through extensive experiments on five benchmark datasets, achieving better or comparable anomaly detection results against strong baseline methods. We also demonstrate through an ablation study that skip connection helps improve the model performance.

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes available. A much cheaper and more efficient solution would be to enable the continual pre-training of these models, i.e. updating pre-trained models with new data instead of re-training them from scratch. However, the distribution shift induced by novel data typically results in degraded performance on past data. Taking a step towards efficient continual pre-training, in this work, we examine the effect of different warm-up strategies. Our hypothesis is that the learning rate must be re-increased to improve compute efficiency when training on a new dataset. We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule. We conduct all experiments on the Pythia 410M language model architecture and evaluate performance through validation perplexity. We experiment with different pre-training checkpoints, various maximum learning rates, and various warmup lengths. Our results show that while rewarming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch$\unicode{x2013}$even for a large downstream dataset.

This paper introduces the Trinary decision tree, an algorithm designed to improve the handling of missing data in decision tree regressors and classifiers. Unlike other approaches, the Trinary decision tree does not assume that missing values contain any information about the response. Both theoretical calculations on estimator bias and numerical illustrations using real data sets are presented to compare its performance with established algorithms in different missing data scenarios (Missing Completely at Random (MCAR), and Informative Missingness (IM)). Notably, the Trinary tree outperforms its peers in MCAR settings, especially when data is only missing out-of-sample, while lacking behind in IM settings. A hybrid model, the TrinaryMIA tree, which combines the Trinary tree and the Missing In Attributes (MIA) approach, shows robust performance in all types of missingness. Despite the potential drawback of slower training speed, the Trinary tree offers a promising and more accurate method of handling missing data in decision tree algorithms.

We present a novel approach for efficiently generating quantum feature maps for quantum-enhanced support vector machines, a kernel-based classifier, enabling access to high-dimensional Hilbert space. Our method employs a multi-objective genetic algorithm that simultaneously maximizes classification accuracy while minimizing both the local and non-local gate costs of the quantum feature map's circuit. To achieve this, we define distinct fitness functions for local gates and entanglement gates. Comparisons with classical classifiers are given in order to understand the advantages of using quantum machine learning. Surprisingly, our experiments reveal that the optimal configuration of quantum circuits for the quantum kernel method incorporates a proportional number of non-local gates for entanglement, contrary to previous literature where non-local gates were largely suppressed. Furthermore, we demonstrate that the separability indexes of data can be effectively leveraged to determine the number of non-local gates required for the quantum support vector machine's feature maps. This insight can significantly aid in selecting appropriate parameters, such as the entanglement parameter, in various quantum programming packages like quiskit.org based on data analysis. Our findings offer valuable guidance for enhancing the efficiency and accuracy of quantum machine learning algorithms.

Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.

Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multi-objective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning~(RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Videos: https://sites.google.com/view/naturalwalkingrl

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res, https://huggingface.co/datasets/LEAP/ClimSim_low-res, and https://huggingface.co/datasets/LEAP/ClimSim_low-res_aqua-planet) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

Attention mechanism is a central component of the transformer architecture which led to the phenomenal success of large language models. However, the theoretical principles underlying the attention mechanism are poorly understood, especially its nonconvex optimization dynamics. In this work, we explore the seminal softmax-attention model $f(\boldsymbol{X})=\langle \boldsymbol{Xv}, \texttt{softmax}(\boldsymbol{XWp})\rangle$, where $\boldsymbol{X}$ is the token sequence and $(\boldsymbol{v},\boldsymbol{W},\boldsymbol{p})$ are trainable parameters. We prove that running gradient descent on $\boldsymbol{p}$, or equivalently $\boldsymbol{W}$, converges in direction to a max-margin solution that separates $\textit{locally-optimal}$ tokens from non-optimal ones. This clearly formalizes attention as an optimal token selection mechanism. Remarkably, our results are applicable to general data and precisely characterize $\textit{optimality}$ of tokens in terms of the value embeddings $\boldsymbol{Xv}$ and problem geometry. We also provide a broader regularization path analysis that establishes the margin maximizing nature of attention even for nonlinear prediction heads. When optimizing $\boldsymbol{v}$ and $\boldsymbol{p}$ simultaneously with logistic loss, we identify conditions under which the regularization paths directionally converge to their respective hard-margin SVM solutions where $\boldsymbol{v}$ separates the input features based on their labels. Interestingly, the SVM formulation of $\boldsymbol{p}$ is influenced by the support vector geometry of $\boldsymbol{v}$. Finally, we verify our theoretical findings via numerical experiments and provide insights.

Recent works have demonstrated that the sample complexity of gradient-based learning of single index models, i.e. functions that depend on a 1-dimensional projection of the input data, is governed by their information exponent. However, these results are only concerned with isotropic data, while in practice the input often contains additional structure which can implicitly guide the algorithm. In this work, we investigate the effect of a spiked covariance structure and reveal several interesting phenomena. First, we show that in the anisotropic setting, the commonly used spherical gradient dynamics may fail to recover the true direction, even when the spike is perfectly aligned with the target direction. Next, we show that appropriate weight normalization that is reminiscent of batch normalization can alleviate this issue. Further, by exploiting the alignment between the (spiked) input covariance and the target, we obtain improved sample complexity compared to the isotropic case. In particular, under the spiked model with a suitably large spike, the sample complexity of gradient-based training can be made independent of the information exponent while also outperforming lower bounds for rotationally invariant kernel methods.

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec's simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning.

Over the last decades, Stochastic Gradient Descent (SGD) has been intensively studied by the Machine Learning community. Despite its versatility and excellent performance, the optimization of large models via SGD still is a time-consuming task. To reduce training time, it is common to distribute the training process across multiple devices. Recently, it has been shown that the convergence of asynchronous SGD (ASGD) will always be faster than mini-batch SGD. However, despite these improvements in the theoretical bounds, most ASGD convergence-rate proofs still rely on a centralized parameter server, which is prone to become a bottleneck when scaling out the gradient computations across many distributed processes. In this paper, we present a novel convergence-rate analysis for decentralized and asynchronous SGD (DASGD) which does not require partial synchronization among nodes nor restrictive network topologies. Specifically, we provide a bound of $\mathcal{O}(\sigma\epsilon^{-2}) + \mathcal{O}(QS_{avg}\epsilon^{-3/2}) + \mathcal{O}(S_{avg}\epsilon^{-1})$ for the convergence rate of DASGD, where $S_{avg}$ is the average staleness between models, $Q$ is a constant that bounds the norm of the gradients, and $\epsilon$ is a (small) error that is allowed within the bound. Furthermore, when gradients are not bounded, we prove the convergence rate of DASGD to be $\mathcal{O}(\sigma\epsilon^{-2}) + \mathcal{O}(\sqrt{\hat{S}_{avg}\hat{S}_{max}}\epsilon^{-1})$, with $\hat{S}_{max}$ and $\hat{S}_{avg}$ representing a loose version of the average and maximum staleness, respectively. Our convergence proof holds for a fixed stepsize and any non-convex, homogeneous, and L-smooth objective function. We anticipate that our results will be of high relevance for the adoption of DASGD by a broad community of researchers and developers.

This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{SELU}$, $\mathtt{Softplus}$, $\mathtt{GELU}$, $\mathtt{SiLU}$, $\mathtt{Swish}$, $\mathtt{Mish}$, $\mathtt{Sigmoid}$, $\mathtt{Tanh}$, $\mathtt{Arctan}$, $\mathtt{Softsign}$, $\mathtt{dSiLU}$, and $\mathtt{SRS}$. We demonstrate that for any activation function $\varrho\in \mathscr{A}$, a $\mathtt{ReLU}$ network of width $N$ and depth $L$ can be approximated to arbitrary precision by a $\varrho$-activated network of width $4N$ and depth $2L$ on any bounded set. This finding enables the extension of most approximation results achieved with $\mathtt{ReLU}$ networks to a wide variety of other activation functions, at the cost of slightly larger constants.

Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that does not require conditioning on retrieved external knowledge nor additional fine-tuning. Our approach obtains the next-token distribution by contrasting the differences in logits obtained from projecting the later layers versus earlier layers to the vocabulary space, exploiting the fact that factual knowledge in an LLMs has generally been shown to be localized to particular transformer layers. We find that this Decoding by Contrasting Layers (DoLa) approach is able to better surface factual knowledge and reduce the generation of incorrect facts. DoLa consistently improves the truthfulness across multiple choices tasks and open-ended generation tasks, for example improving the performance of LLaMA family models on TruthfulQA by 12-17% absolute points, demonstrating its potential in making LLMs reliably generate truthful facts.

In recent years, understanding the implicit regularization of neural networks (NNs) has become a central task in deep learning theory. However, implicit regularization is itself not completely defined and well understood. In this work, we attempt to mathematically define and study implicit regularization. Importantly, we explore the limitations of a common approach to characterizing implicit regularization using data-independent functions. We propose two dynamical mechanisms, i.e., Two-point and One-point Overlapping mechanisms, based on which we provide two recipes for producing classes of one-hidden-neuron NNs that provably cannot be fully characterized by a type of or all data-independent functions. Following the previous works, our results further emphasize the profound data dependency of implicit regularization in general, inspiring us to study in detail the data dependency of NN implicit regularization in the future.

Interpolators are unstable. For example, the mininum $\ell_2$ norm least square interpolator exhibits unbounded test errors when dealing with noisy data. In this paper, we study how ensemble stabilizes and thus improves the generalization performance, measured by the out-of-sample prediction risk, of an individual interpolator. We focus on bagged linear interpolators, as bagging is a popular randomization-based ensemble method that can be implemented in parallel. We introduce the multiplier-bootstrap-based bagged least square estimator, which can then be formulated as an average of the sketched least square estimators. The proposed multiplier bootstrap encompasses the classical bootstrap with replacement as a special case, along with a more intriguing variant which we call the Bernoulli bootstrap. Focusing on the proportional regime where the sample size scales proportionally with the feature dimensionality, we investigate the out-of-sample prediction risks of the sketched and bagged least square estimators in both underparametrized and overparameterized regimes. Our results reveal the statistical roles of sketching and bagging. In particular, sketching modifies the aspect ratio and shifts the interpolation threshold of the minimum $\ell_2$ norm estimator. However, the risk of the sketched estimator continues to be unbounded around the interpolation threshold due to excessive variance. In stark contrast, bagging effectively mitigates this variance, leading to a bounded limiting out-of-sample prediction risk. To further understand this stability improvement property, we establish that bagging acts as a form of implicit regularization, substantiated by the equivalence of the bagged estimator with its explicitly regularized counterpart. We also discuss several extensions.

Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.

The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.

Deep neural networks employing error back-propagation for learning can suffer from exploding and vanishing gradient problems. Numerous solutions have been proposed such as normalisation techniques or limiting activation functions to linear rectifying units. In this work we follow a different approach which is particularly applicable to closed-loop learning of forward models where back-propagation makes exclusive use of the sign of the error signal to prime the learning, whilst a global relevance signal modulates the rate of learning. This is inspired by the interaction between local plasticity and a global neuromodulation. For example, whilst driving on an empty road, one can allow for slow step-wise optimisation of actions, whereas, at a busy junction, an error must be corrected at once. Hence, the error is the priming signal and the intensity of the experience is a modulating factor in the weight change. The advantages of this Prime and Modulate paradigm is twofold: it is free from normalisation and it makes use of relevant cues from the environment to enrich the learning. We present a mathematical derivation of the learning rule in z-space and demonstrate the real-time performance with a robotic platform. The results show a significant improvement in the speed of convergence compared to that of the conventional back-propagation.

The exclusive or (xor) function is one of the simplest examples that illustrate why nonlinear feedforward networks are superior to linear regression for machine learning applications. We review the xor representation and approximation problems and discuss their solutions in terms of probabilistic logic and associative copula functions. After briefly reviewing the specification of feedforward networks, we compare the dynamics of learned error surfaces with different activation functions such as RELU and tanh through a set of colorful three-dimensional charts. The copula representations extend xor from Boolean to real values, thereby providing a convenient way to demonstrate the concept of cross-validation on in-sample and out-sample data sets. Our approach is pedagogical and is meant to be a machine learning prolegomenon.

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as $\mathcal{O}(T^2 \times \text{polylog}(n))$, where $n$ is the size of the models and $T$ is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been recently extended to exploit prior knowledge on the cardinality of each cluster. Such knowledge is used to increase performance as well as solution quality. In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC. For the lower bound routine, we use the semidefinite programming (SDP) relaxation recently proposed by Rujeerapaiboon et al. [SIAM J. Optim. 29(2), 1211-1239, (2019)]. However, this relaxation can be used in a branch-and-cut method only for small-size instances. Therefore, we derive a new SDP relaxation that scales better with the instance size and the number of clusters. In both cases, we strengthen the bound by adding polyhedral cuts. Benefiting from a tailored branching strategy which enforces pairwise constraints, we reduce the complexity of the problems arising in the children nodes. For the upper bound, instead, we present a local search procedure that exploits the solution of the SDP relaxation solved at each node. Computational results show that the proposed algorithm globally solves, for the first time, real-world instances of size 10 times larger than those solved by state-of-the-art exact methods.

Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the implicit nature of the scene representation. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses via the cross-attention mechanism as the pre-training scheme. Generating view images has more precise supervision than its point cloud counterpart, thus assisting 3D backbones to have a finer comprehension of the geometrical structure and stereoscopic relations of the point cloud. Experimental results have proved the superiority of our proposed 3D-to-2D generative pre-training over previous pre-training methods. Our method is also effective in boosting the performance of architecture-oriented approaches, achieving state-of-the-art performance when fine-tuning on ScanObjectNN classification and ShapeNetPart segmentation tasks. Code is available at https://github.com/wangzy22/TAP.

Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command signals (e.g., from a brain-computer interface). Recent advances in human-in-the-loop machine learning enable such systems to improve by interacting with users, but tend to be limited by the amount of data that they can collect from individual users in practice. In this paper, we propose a reinforcement learning algorithm to address this by training an interface to map raw command signals to actions using a combination of offline pre-training and online fine-tuning. To address the challenges posed by noisy command signals and sparse rewards, we develop a novel method for representing and inferring the user's long-term intent for a given trajectory. We primarily evaluate our method's ability to assist users who can only communicate through noisy, high-dimensional input channels through a user study in which 12 participants performed a simulated navigation task by using their eye gaze to modulate a 128-dimensional command signal from their webcam. The results show that our method enables successful goal navigation more often than a baseline directional interface, by learning to denoise user commands signals and provide shared autonomy assistance. We further evaluate on a simulated Sawyer pushing task with eye gaze control, and the Lunar Lander game with simulated user commands, and find that our method improves over baseline interfaces in these domains as well. Extensive ablation experiments with simulated user commands empirically motivate each component of our method.

We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses. The proposed model enables direct modeling of specific human identities through a deep generative architecture, which can produce humans in any arbitrary pose. It is the first of its kind to have been trained end-to-end using only synthetically generated data, which not only provides highly accurate human mesh representations but also allows for precise anthropometry of the body. Moreover, using a highly diverse animation library, we articulated our synthetic humans' body and hands to maximize the diversity of the learnable priors for model training. Our model was trained on a dataset of $100k$ procedurally-generated posed human meshes and their corresponding anthropometric measurements. Our synthetic data generator can be used to generate millions of unique human identities and poses for non-commercial academic research purposes.

Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of the geometric features embedded in the images since the network cannot derive any object boundary information from just image-level labels. We define a boundary here as the line separating an object and its background, or two different objects. To address this drawback, we are proposing our novel ReFit framework, which deploys state-of-the-art class activation maps combined with various post-processing techniques in order to achieve fine-grained higher-accuracy segmentation masks. To achieve this, we investigate a state-of-the-art unsupervised segmentation network that can be used to construct a boundary map, which enables ReFit to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that our results are reproducible, and accessible online at https://github.com/bharathprabakaran/ReFit.

Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples. However, it has been noted that maximizing the log-likelihood within an energy-based setting can lead to an adversarial framework where the discriminator provides unnormalized density (often called energy). We further develop this perspective, incorporate importance sampling, and show that 1) Wasserstein GAN performs a biased estimate of the partition function, and we propose instead to use an unbiased estimator; and 2) when optimizing for likelihood, one must maximize generator entropy. This is hypothesized to provide a better mode coverage. Different from previous works, we explicitly compute the density of the generated samples. This is the key enabler to designing an unbiased estimator of the partition function and computation of the generator entropy term. The generator density is obtained via a new type of flow network, called one-way flow network, that is less constrained in terms of architecture, as it does not require a tractable inverse function. Our experimental results show that our method converges faster, produces comparable sample quality to GANs with similar architecture, successfully avoids over-fitting to commonly used datasets and produces smooth low-dimensional latent representations of the training data.